From 8dbbf0ae9b1317db61e19d05e0d039023a6690e3 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Sat, 23 Sep 2023 13:01:00 +0200
Subject: [PATCH 01/42] Update vignette up to centroiding.

---
 vignettes/images/LC-MS-drawing.gif | Bin 0 -> 2059357 bytes
 vignettes/xcms-preprocessing.Rmd   | 260 ++++++++++++++++-------------
 2 files changed, 146 insertions(+), 114 deletions(-)
 create mode 100644 vignettes/images/LC-MS-drawing.gif

diff --git a/vignettes/images/LC-MS-drawing.gif b/vignettes/images/LC-MS-drawing.gif
new file mode 100644
index 0000000000000000000000000000000000000000..fcf51593e09cc91fdfec9406b598d947aa038b44
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diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index 5574f20..bf7b677 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -1,10 +1,10 @@
 ---
 title: "Exploring and Analyzing LC-MS data with *Spectra* and *xcms*"
 author:
-- name: "Johannes Rainer"
+- name: "Philippine Louail, Johannes Rainer"
   affiliation: "Eurac Research, Bolzano, Italy; johannes.rainer@eurac.edu github: jorainer"
 graphics: yes
-date: "September 2023"
+date: "October 2023"
 output:
   BiocStyle::html_document:
     number_sections: true
@@ -24,7 +24,7 @@ library("BiocStyle")
 library("knitr")
 library("rmarkdown")
 opts_chunk$set(message = FALSE, error = FALSE, warning = FALSE,
-               cache = FALSE, fig.width = 7, fig.height = 7)
+               cache = FALSE, fig.width = 10, fig.height = 7)
 ```
 
 # Abstract
@@ -34,7 +34,7 @@ data handling and exploration using the `r Biocpkg("MsExperiment")` and `r
 Biocpkg("Spectra")` Bioconductor packages and perform the preprocessing of a
 small LC-MS data set using the `r Biocpkg("xcms")` package. Functionality from
 the `r Biocpkg("MetaboCoreUtils")` and `r Biocpkg("MsCoreUtils")` packages are
-used for general tasks frequently performed in metabolomics data
+used for general tasks frequently performed during metabolomics data
 analysis. Ultimately, the functionality from these packages can be combined to
 build custom, data set-specific (and reproducible) analysis workflows.
 
@@ -43,8 +43,7 @@ which is followed by the description of a simple data centroiding approach and
 finally we present an *xcms*-based LC-MS data preprocessing that comprises
 chromatographic peak detection, alignment and correspondence. Data normalization
 procedures, compound identification and differential abundance analysis are not
-covered. Particular emphasis is given on deriving and defining data-set
-dependent values for the most critical *xcms* preprocessing parameters.
+covered here. Particular emphasis is given on deriving and defining data set-dependent values for the most critical *xcms* preprocessing parameters.
 
 
 # Introduction
@@ -52,15 +51,16 @@ dependent values for the most critical *xcms* preprocessing parameters.
 Preprocessing is the first step in the analysis of *untargeted* LC-MS or gas
 chromatography (GC)-MS data. The aim of the preprocessing is the quantification
 of signals from ions measured in a sample, adjusting for any potential retention
-time drifts between samples and the matching of the quantified signal across
-samples within an experiment. The resulting two-dimensional matrix with
+time drifts between samples followed by the matching of the quantified signal
+across samples within an experiment. The resulting two-dimensional matrix with
 abundances of the so called *LC-MS features* in all samples can then be further
 processed, e.g. by normalizing the data to remove differences due to sample
-processing, batch effects or injection order-dependent signal drifts. After
-preprocessing, the LC-MS features that are usually only characterized by their
-mass-to-charge ration (*m/z*) and retention time, have to be annotated to the
-actual ions and metabolites they represent. Data normalization and annotation
-are not covered in this document.
+processing, batch effects or injection order-dependent signal drifts. LC-MS
+features are usually only characterized by their mass-to-charge ration (*m/z*)
+and retention time and hence need to be annotated to the actual ions and
+metabolites they represent. Data normalization and annotation are not covered in
+here but links to related tutorials and workshops are provided at the
+end of the document.
 
 
 ## Mass spectrometry
@@ -77,15 +77,15 @@ Many ions will result, when measured with MS alone, in a very similar
 therefore frequently coupled with a second technology to separate them prior
 quantification based on properties other than their mass (e.g. based on their
 polarity). Common choices are gas chromatography (GC) or liquid chromatography
-(LC). In a typical LC-MS setup a samples gets injected into the system,
-molecules are separated in the LC column while the MS instruments continuously
-(at discrete time points) continues to measure all ions that get generated from
-the molecules eluting at different time points from the LC-column. Molecules get
-thus separated on two different dimensions, the retention time dimension (from
-the LC) and the mass-to-charge dimension (from the MS) making it easier to
-measure and identify molecules in more complex samples.
+(LC). In a typical LC-MS setup a sample gets injected into the system, the
+molecules in the sample are separated in the LC column, get ionized and then
+measured (at discrete time points) by the MS instrument (see Figure below for a
+simple visualization). Molecules get thus separated on two different dimensions,
+the retention time dimension (from the LC) and the mass-to-charge dimension
+(from the MS) making it easier to measure and identify molecules in more complex
+samples.
 
-![](images/LCMS.png)
+![](images/LC-MS-drawing.gif)
 
 In such GC/LC-MS based untargeted metabolomics experiments, the data is analyzed
 along the retention time dimension and *chromatographic* peaks (which are
@@ -105,11 +105,11 @@ Naming conventions and terms used in this document are:
 - *alignment*: process that adjusts for retention time differences
   (i.e. possible signal drifts from the LC) between measurements/files.
 - *correspondence*: grouping of chromatographic peaks (presumably from the same
-  ion) across files.
+  ion) across samples/files.
 - *feature* (or *LC-MS features*): entity representing signal from the same type
   of ion/molecule, characterized by its specific retention time and *m/z*. In
   *xcms*, features represent identified chromatographic peaks grouped across
-  files/samples.
+  samples/files.
 
 
 # Data import and exploration
@@ -119,21 +119,23 @@ signals from pooled human serum samples measured with a ultra high performance
 liquid chromatography (UHPLC) system (Agilent 1290) coupled with a Q-TOF MS
 (TripleTOF 5600+ AB Sciex) instrument. Chromatographic separation was based on
 hydrophilic interaction liquid chromatography (HILIC) separating metabolites
-depending on their polarity. The input files contain all signals measured by
-the MS instrument (so called *profile mode* data). To reduce file sizes, the
-data set was restricted to an *m/z* range from 105 to 134 and retention times
-from 0 to 260 seconds.
+depending on their polarity. The input files contain all signals measured by the
+MS instrument (so called *profile mode* data). To reduce file sizes, the data
+set was restricted to an *m/z* range from 105 to 134 and retention times from 0
+to 260 seconds. Both QC pool samples were taken from a larger experiment and
+were injected in the same measurement run at different time points (injected in
+position 1 and 19 of the measurement run).
 
 In the code block below we first load all required libraries and define the
-location of the mzML files, which are part of the *msdata* R package. We also
-define a `data.frame` with the names of the mzML files, an arbitrary sample
-name, the index in which the respective sample was measured within the actual
-LC-MS run and the sample group of the samples. It is generally suggested to
-provide all experiment-relevant phenotypic and technical information through
-such a data frame. Ideally, such data frame could be defined in an xls sheet
-that could then be imported with the `read_xlsx` function from the *readxl* R
-package. This data frame is then passed, along with the file names, to the
-`readMsExperiment` call to import the data.
+location of the mzML files, which are distributed through the *msdata* R
+package. We also define a `data.frame` with the names of the mzML files, an
+arbitrary sample name, the index in which the respective sample was measured
+within the LC-MS run and the sample *group* of the samples. It is generally
+suggested to provide all experiment-relevant phenotypic and technical
+information through such a data frame. Also, the data frame could be defined in
+an xls sheet that could then be imported with the `read_xlsx` function from the
+*readxl* R package. This data frame is then passed, along with the file names,
+to the `readMsExperiment` call to import the data.
 
 ```{r load-data}
 library(xcms)
@@ -153,14 +155,14 @@ data <- readMsExperiment(fls, sampleData = pd)
 data
 ```
 
-The MS data of the experiment is now *represented* by a `MsExperiment` object.
+The MS data of the experiment is now *represented* by an `MsExperiment` object.
 
 
-## Basic data access and visualization
+## Basic data access
 
 The `MsExperiment` object manages the *linkage* between samples and spectra.
-The `length` of an `MsExperiment` is defined by the number of samples within the
-object.
+The `length` of an `MsExperiment` is defined by the number of samples (files)
+within the object.
 
 ```{r general-access}
 length(data)
@@ -197,14 +199,15 @@ spectra(data)
 The new version of *xcms* uses thus the more modern and flexible infrastructure
 for MS data analysis provided by the `r Biocpkg("Spectra")` package. While it is
 still possible and supported to use *xcms* together with the `r
-Biocpkg("MSnbase")`, users are advised to switch to this new infrastructure as
-it provides more flexibility and a higher performance.
+Biocpkg("MSnbase")` package, users are advised to switch to this new
+infrastructure as it provides more flexibility and a higher performance.
 
 This `Spectra` object represents the full LC-MS data of the experiment. Each
 element in this object is a spectrum (in one sample/file) with all information
 provided by the respective original data (mzML) file. Note that, through
 additional packages such as the `r Biocpkg("MsBackendRawFileReader")`, it would
-also be possible import MS data from other files than mzML, mzXML or CDF files.
+also be possible to import MS data from other files than mzML, mzXML or CDF
+files.
 
 In the next few examples we briefly explain the `Spectra` object and illustrate
 the use of such objects using some simple examples. More information on
@@ -213,10 +216,10 @@ the use of such objects using some simple examples. More information on
 [SpectraTutorials](https://jorainer.github.io/SpectraTutorials). Besides the
 peak data (*m/z* and intensity values) also additional spectra variables
 (metadata) are available in a `Spectra` object. These can be listed using the
-`spectraVariables` function that we call on our example MS data. To avoid nested
-function calls and hence improve the readability of the code, we use the R pipe
-operator `|>` that allows to concatenate consecutive calls in a more readable
-fashion.
+`spectraVariables` function that we call on our example MS data below. To avoid
+nested function calls and hence improve the readability of the code, we use the
+R pipe operator `|>` that allows to concatenate consecutive calls in a more
+readable fashion.
 
 ```{r}
 #' List available spectra variables
@@ -224,12 +227,14 @@ spectra(data) |>
     spectraVariables()
 ```
 
-For most of these spectra variables dedicated accessor functions are available
-(such as `msLevel`, `rtime`) and in addition it is possible to access any
-variable using `$` and the name of the variable (like accessing the columns of
-a `data.frame`). As an example we extract below the `msLevel` spectra variable
-and use the `table` function on the result to get an overview of the number of
-spectra from different MS levels available in the object.
+Thus, for all spectra we have general information such as the MS level
+(`"msLevel"`) or the retention time (`"rtime"`) available. For most of these
+spectra variables dedicated accessor functions are available (such as `msLevel`,
+`rtime`) and in addition it is possible to access any variable using `$` and the
+name of the variable (like accessing the columns of a `data.frame`). As an
+example we extract below the `msLevel` spectra variable and use the `table`
+function on the result to get an overview of the number of spectra from
+different MS levels available in the object.
 
 ```{r}
 #' List number of spectra per MS level
@@ -245,10 +250,10 @@ files, first all spectra from the first file and then from the second, all in
 the order in which they are provided in the original data files (hence generally
 ordered by retention time). To access spectra from a specific sample we either
 subset the `MsExperiment` to that particular sample or we use the `fromFile`
-function that returns the indices of the file within the `MsExperiment` to which
-each spectra belongs. Below we subset our object to the first data file, extract
-the retention times of the spectra from that file and, by using the `head`
-function, display the first 6 of them.
+function that returns for each spectrum the index of the file within the
+`MsExperiment` to which it belongs. Below we subset our object to the first data
+file, extract the retention times of the spectra from that file and, by using
+the `head` function, display the first 6 of them.
 
 ```{r}
 #' Display the first 6 retention times of spectra from the first sample
@@ -274,9 +279,8 @@ data |>
 
 We could also check the number of peaks per spectrum in the different data
 files. The number of peaks per spectrum can be extracted with the `lengths`
-function. Below we are extracting these values, splitting these values by file
-and then calculate the quartiles of the peak counts using the `quantile`
-function.
+function. Below we extract these values, split them by file and then calculate
+the quartiles of the peak counts using the `quantile` function.
 
 ```{r}
 #' Get the distribution of peak counts per file
@@ -287,8 +291,8 @@ spectra(data) |>
 
 ```
 
-Thus, for the present data set, the number of spectra and also the number of
-peaks per spectra are comparable.
+Thus, for the present data set, the number of spectra and also the average
+number of peaks per spectra are comparable.
 
 Individual MS spectra can be accessed by subsetting the `Spectra` object
 returned by `spectra`. As an example we below subset the data to the second
@@ -320,9 +324,19 @@ intensity values of a spectrum as a (two column) numeric matrix.
 peaksData(sp)[[1]] |> head()
 ```
 
-Total ion signal per spectrum could be calculated simply by summing the
-intensities of all peaks per spectrum. Below we determine the quartiles of the
-total ion signal for the first file.
+The total ion signal of a spectrum could be calculated by simply summing the
+intensities of all peaks in the spectrum. Below we perform that operation on the
+spectrum extracted above.
+
+```{r}
+#' Calculate total ion signal for the extracted spectrum
+intensity(sp) |>
+    sum()
+```
+
+The same operation can also be applied to the full data set. As an example we
+calculate below the total ion signal for each spectrum in the first file and
+determine the distribution of these using the `quantiles` function.
 
 ```{r}
 #' Calculate the distribution of total ion signal of the first file
@@ -332,7 +346,7 @@ spectra(data[1]) |>
     quantile()
 ```
 
-We repeat that for the second file.
+We repeat the operation for the second file.
 
 ```{r}
 #' Repeat for the second file
@@ -342,28 +356,29 @@ spectra(data[2]) |>
     quantile()
 ```
 
-Through the `Spectra` object we have thus the possibility to inspect and explore
-the (raw) MS data of an experiment and use its functionality to create own
-quality assessment functions. Alternatively, also the `r Biocpkg("MsQuality")`
-package could be to calculate core MS quality metrics on a full experiment
+The total ion signals of the two data files is (as expected) similar.  Through
+the `Spectra` object we have thus the possibility to inspect and explore the
+(raw) MS data of an experiment and use its functionality to create own quality
+assessment functions. Alternatively, also the `r Biocpkg("MsQuality")` package
+could be to calculate core MS quality metrics on a full experiment
 (`MsExperiment`) or individual data files (`Spectra`). TODO: ADD REFERENCE TO
 MSQUALITY PAPER.
 
 
-# Data visualization
+## Data visualization
 
 Visualization is crucial for quality assessment of MS data. For LC-MS data
 visualizing a base peak chromatogram (BPC) or total ion chromatogram (TIC) would
 for example allow to evaluate the performance of the liquid chromatography of
 the various samples in an experiment. To create such a plot we below extract the
 BPC from our data. The BPC extracts the maximum peak signal from each spectrum
-in a data file and plots this information (on the y-axis) against the retention
-time for that spectrum. While we could also extract these values similarly to
-the total ion intensity in the previous section, we use below the `chromatogram`
-function that allows extraction of chromatographic data from MS data. With
-parameter `aggregationFun = "max"` we define to report the maximum signal per
-spectrum (setting `aggregationFun = "sum"` would for example sum up all
-intensities of a spectrum and hence create a TIC).
+in a data file and allows thus to plot this information (on the y-axis) against
+the retention time for that spectrum. While we could also extract these values
+similarly to the total ion intensity in the previous section, we use below the
+`chromatogram` function that allows extraction of chromatographic data from MS
+data. With parameter `aggregationFun = "max"` we define to report the maximum
+signal per spectrum (setting `aggregationFun = "sum"` would in contrast sum up
+all intensities of a spectrum and hence create a TIC).
 
 ```{r}
 #' Extract and plot a BPC
@@ -383,7 +398,7 @@ intensity) into 2 dimensions (retention time by intensity). An orthogonal
 visualization to this would be a *base peak spectrum* (BPS) that collapses the
 data in retention time dimension. Such a visualization could provide information
 on the most abundant masses (or rather mass-to-charge values) in the data set
-(regardless of the retention time in which they are measured). In contrast to
+(regardless of the retention time in which they were measured). In contrast to
 the BPC it is however not straight forward to create such a visualization: mass
 peaks, even if representing signal from the same ion, will never be identical
 between consecutive spectra, but will slightly differ based on the measurement
@@ -402,19 +417,19 @@ aggregate/combine sets of spectra into a single spectrum. By default, this
 function will combine sets of spectra (that can be defined with parameter `f`)
 creating an union of the peaks present in spectra of a set. For mass peaks with
 a similar *m/z* value (depending on parameter `ppm`) the peaks' intensities are
-aggregated using the `intensityFun` function and only one peak is reported. With
-the setting below we combine all spectra from one file (by using `f =
-fromFile(data)`) into a single spectrum containing mass peaks present in any of
-the spectra of that file. Mass peaks with a difference in their *m/z* that
-is smaller than `ppm` (parts-per-million of the *m/z* value) are combined into
-one peak for which the maximal intensity of the grouped peaks is reported. Note
-that we set `ppm` to a rather small value because the present data is in profile
-mode.
+aggregated using the function defined with parameter `intensityFun` and only one
+peak is reported. With the setting below we combine all spectra from one file
+(by using `f = fromFile(data)`) into a single spectrum containing mass peaks
+present in any of the spectra of that file. Mass peaks with a difference in
+their *m/z* that is smaller than `ppm` (parts-per-million of the *m/z* value)
+are combined into one peak for which the maximal intensity of the grouped peaks
+is reported. Note that we set `ppm` to a rather small value because the present
+data is in profile mode.
 
 ```{r}
 #' Combine all spectra of one file into a single spectrum
-bps <- combineSpectra(spectra(data), f = fromFile(data),
-                      intensityFun = max, ppm = 5)
+bps <- spectra(data) |>
+    combineSpectra(f = fromFile(data), intensityFun = max, ppm = 5)
 bps
 ```
 
@@ -422,6 +437,7 @@ bps
 files. Below we plot these.
 
 ```{r, fig.cap = "Base peak spectrum for each of the two samples."}
+#' Plot the BPS
 plotSpectra(bps)
 ```
 
@@ -434,9 +450,10 @@ to explore the data in more detail. To this end we next focus on a specific
 subset of the data were we expect signal for a compound that should be present
 in serum samples (such as ions of the molecule serine). With the particular
 LC-MS setup used for the present samples, ions for this metabolite are expected
-to elute at about 180 seconds. We thus filter below the spectra data using the
-`filterRt` function extracting only spectra measured between 180 and 181
-seconds.
+to elute at about 180 seconds (this retention time was determined by measuring a
+pure standard for this compound on the same LC-MS setup). We thus filter below
+the spectra data using the `filterRt` function extracting only spectra measured
+between 180 and 181 seconds.
 
 ```{r}
 sps <- spectra(data) |>
@@ -488,18 +505,18 @@ mass_serine <- calculateMass("C3H7NO3")
 mass_serine
 ```
 
-The *native* serine molecule is however uncharged and thus can not be measured
-by mass spectrometry. In order to be detectable, molecules need to be first
-ionized before being injected in an MS instrument. While different ions can (and
-will) be generated for a molecule, one of the most commonly created ions in
-positive polarity is the *[M+H]+* ion (protonated ion). To calculate the *m/z*
-values for specific ions/adducts of molecules, we can use the `mass2mz`
-function, also from the *MetaboCoreUtils* package. Below we calculate the *m/z*
-for the *[M+H]+* ion of serine providing the monoisotopic mass of that molecule
-and specifying the adduct we are interested in. Also other types of adducts are
-supported. These could be listed with the `adductNames` function
-(`adductNames()` for all positively charged and `adductNames("negative")` for
-all negatively charge ions).
+The *native* serine molecule is however uncharged and can thus not be measured
+by mass spectrometry. In order to be detectable, molecules need to be ionized
+before being injected in an MS instrument. While different ions can (and will)
+be generated for a molecule, one of the most commonly generated ions in positive
+polarity is the *[M+H]+* ion (protonated ion). To calculate the *m/z* values for
+specific ions/adducts of molecules, we can use the `mass2mz` function, also from
+the *MetaboCoreUtils* package. Below we calculate the *m/z* for the *[M+H]+* ion
+of serine providing the monoisotopic mass of that molecule and specifying the
+adduct we are interested in. Also other types of adducts are supported. These
+could be listed with the `adductNames` function (`adductNames()` for all
+positively charged and `adductNames("negative")` for all negatively charge
+ions).
 
 ```{r}
 serine_mz <- mass2mz(mass_serine, "[M+H]+")
@@ -534,24 +551,30 @@ extracting such chromatograms for the *m/z* of interest and the full retention
 time range can help determining its likely retention time.
 
 The object returned by the `chromatogram` function arranges the individual
-`MChromatogram` objects (one per sample) in a two-dimensional array, columns
-being samples (files) and rows data slices (i.e. *m/z* - rt ranges). Note that
-this type of data representation, defined in the `r Biocpkg("MSnbase")` package,
-is likely to be replaced in future with a more efficient and flexible data
-structure similar to `Spectra`. Data from the individual chromatograms can be
-accessed using the `intensity` and `rtime` functions (similar to the `mz` and
-`intensity` functions for a `Spectra` object).
+`MChromatogram` objects (each representing the chromatographic data consisting
+of pairs of retention time and intensity values of one sample) in a
+two-dimensional array, columns being samples (files) and rows data slices (i.e.,
+*m/z* - rt ranges). Note that this type of data representation, defined in the
+`r Biocpkg("MSnbase")` package, is likely to be replaced in future with a more
+efficient and flexible data structure similar to `Spectra`. Data from the
+individual chromatograms can be accessed using the `intensity` and `rtime`
+functions (similar to the `mz` and `intensity` functions for a `Spectra`
+object).
 
 ```{r chromatogram}
-#' get intensity and retention times for the chromatogram of the first
-#' sample
+#' get intensity valuesfor the chromatogram of the first sample
 intensity(serine_chr[1, 1]) |>
     head()
 
-rts <- rtime(serine_chr[1, 1]) |>
+#' get the respective retention times of the first sample
+rtime(serine_chr[1, 1]) |>
     head()
 ```
 
+Note that an `NA` is reported if in the *m/z* range from which the
+chromatographic data was extracted no intensity was measured at the given
+retention time.
+
 At last we further focus on the tentative signal of serine extracting the ion
 chromatogram restricting on the retention time range containing its
 signal. While we could also pass the retention time and *m/z* range with
@@ -1642,6 +1665,15 @@ workshops/tutorials at
 [SpectraTutorials](https://jorainer.github.io/SpectraTutorials).
 
 
+# Final words
+
+- don't use defaults of `xcms`.
+- use the infrastructure to inspect and summarize the data.
+- don't limit yourself to just `xcms`, use the vast of additional packages,
+  methods, visualizations in R.
+- build custom workflows, data visualization approaches or your own analysis
+  method.
+
 # Appendix
 
 ## Additional visualizations

From 1e0de7542fcfcf85145d5b47c243a1b24e591a48 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Sat, 23 Sep 2023 13:13:44 +0200
Subject: [PATCH 02/42] Update/fix centroiding section

---
 vignettes/xcms-preprocessing.Rmd | 59 +++++++++++++++-----------------
 1 file changed, 27 insertions(+), 32 deletions(-)

diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index bf7b677..18e0f91 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -606,21 +606,16 @@ internal standards) to evaluate the LC-MS data of an experiment.
 # Centroiding of profile MS data
 
 MS instruments allow to export data in profile or centroid mode. Profile data
-
-contains the signal for all discrete *m/z* values (and retention times) for which
-the instrument collected data [@Smith:2014di]. MS instruments continuously
-sample and record signals, therefore a mass peak for a single ion in one
-spectrum will consist of multiple intensities at discrete *m/z* values.
 contains the signal for all discrete *m/z* values (and retention times) for
 which the instrument collected data [@Smith:2014di]. MS instruments continuously
-sample and record signals and a mass peak for a single ion in one spectrum will
-thus consist of a multiple intensities at discrete (but highly similar) *m/z*
-values. The process to reduce this distribution of signals to a single
-representative mass peak (the centroid) is called centroiding. This process
-results in much smaller file sizes, without too much information loss. *xcms*,
-specifically the *centWave* chromatographic peak detection algorithm, was
-designed for centroided data, thus, prior to data analysis, profile data, such
-as the example data used here, should be centroided.
+sample and record signals, therefore a mass peak for a single ion in one
+spectrum will consist of multiple intensities at discrete *m/z* values. The
+process to reduce this distribution of signals to a single representative mass
+peak (the centroid) is called centroiding. This process results in much smaller
+file sizes, with only little information loss. *xcms*, specifically the
+*centWave* chromatographic peak detection algorithm, was designed for centroided
+data, thus, prior to data analysis, profile data, such as the example data used
+here, should be centroided.
 
 Below we inspect the profile data for one of the spectra extracted above and
 focus on the mass peak for serine.
@@ -637,8 +632,8 @@ Instead of a single peak, several mass peaks were recorded by the MS instrument
 with an *m/z* very close to the theoretical *m/z* for the *[M+H]+* ion of serine
 (indicated with a red dotted line).
 
-We can also visualize this information differently: the `plot` function
-for `MsExperiment` generates a two-dimensional visualization of the
+We can also visualize this information differently: the `plot` function for
+`MsExperiment` generates a two-dimensional visualization of the
 three-dimensional LC-MS data: peaks are drawn at their respective location in
 the two-dimensional *m/z* *vs* retention time plane with their intensity being
 color coded. Below we subset the data to the *m/z* - retention time region
@@ -652,12 +647,12 @@ filterMz(mz = c(106.02, 106.07)) |>
 plot()
 ```
 
-The lower panel of the plot shows all mass peaks measured by the instrument,
-each *column* of data points representing the signal measured at one discrete
-time point (i.e. for one spectrum) with the intensities encoded with a color
-scale (blue representing low, yellow high intensity). The upper panel of the
-plot shows a chromatographic visualization of the data from the lower panel,
-i.e., for each retention time (spectrum) the sum of intensities is shown.
+The lower panel of the plot shows all mass peaks measured by the instrument:
+each point represents one mass peak with its intensity being color coded (blue
+representing low, yellow high intensity). Each column of data points represents
+data from the same spectrum. The upper panel of the plot shows a chromatographic
+visualization of the data from the lower panel, i.e., for each retention time
+(spectrum) the sum of intensities is shown.
 
 Note that, while it would be possible to create such a plot for the full MS data
 of an experiment, this type of visualization works best for small *m/z* -
@@ -666,9 +661,9 @@ retention time regions.
 Next, we *smooth* the data in each spectrum using a Savitzky-Golay filter, which
 usually improves data quality by reducing noise. Subsequently we perform the
 centroiding of the data based on a simple peak-picking strategy that reports the
-maximum signal for each mass peak in each spectrum and replace the spectra data
-in our data (`MsExperiment`) object. Finally, we create the previous data
-visualization also on the such centroided data.
+maximum signal for each mass peak in each spectrum. Finally, we replace the
+spectra in data (`MsExperiment`) object with the centroided spectra and
+visualize the result repeating the visualization from above.
 
 ```{r centroiding, fig.cap = "Centroided data for Serine.", fig.width = 10, fig.height = 5, fig.pos = "h!"}
 #' Smooth and centroid the spectra data
@@ -688,9 +683,9 @@ plot()
 
 The impact of the centroiding is clearly visible: each signal for an ion in a
 spectrum was reduced to a single data point. For more advanced centroiding
-options that can also fine-tune the *m/z* value of the reported centroid see the
-documentation of the `pickPeaks` function or the centroiding vignette of the `r
-Biocpkg("MSnbase")` package.
+options, that can also fine-tune the *m/z* value of the reported centroid, see
+the documentation of the `pickPeaks` function or the centroiding vignette of the
+`r Biocpkg("MSnbase")` package.
 
 While we could now simply proceed with the data analysis, we below save the
 centroided MS data to mzML files to also illustrate how the *Spectra* package
@@ -718,8 +713,8 @@ export(spectra(data), backend = MsBackendMzR(),
 
 ```
 
-We can then import the centroided data again from the newly generated mzML
-files and proceed with the analysis.
+We can then import the centroided data again from the newly generated mzML files
+and proceed with the analysis.
 
 ```{r}
 #' Re-import the centroided data.
@@ -729,10 +724,10 @@ fls <- basename(fls)
 data <- readMsExperiment(fls, sampleData = pd)
 ```
 
-This code would thus also allow to create scripts to batch-perform an R-based
-centroiding, which would allow fine tuning of the individual parameters.
+This, or similar, code would allow to create scripts to batch-perform an R-based
+centroiding.
+
 
-LLLLLLLLL
 
 # Preprocessing of LC-MS data
 

From ae4fd9d6e6b3b5d5ed3f403e93a5235f5dd4d9b9 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Sat, 23 Sep 2023 13:38:59 +0200
Subject: [PATCH 03/42] Update peak detection setting

---
 vignettes/xcms-preprocessing.Rmd | 104 +++++++++++++++++--------------
 1 file changed, 57 insertions(+), 47 deletions(-)

diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index 18e0f91..d4decae 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -811,15 +811,16 @@ below).
 plot(serine_chr)
 ```
 
-For serine, the chromatographic peak is thus about 5 seconds wide. We thus adapt
-the `peakwidth` for the present data set and repeat the peak detection using
-these settings. In general, the lower and upper peak width should be set to
-include most of the chromatographic peak widths. For the present data set we set
-the values to 2 to 10 seconds. In addition, by setting `integrate = 2`, we
-select a different peak boundary estimation algorithm. This works particularly
-well for non-gaussian peak shapes and ensures that also signal from peak's tail
-is integrated (eventually re-run the code with the default `integrate = 1` to
-compare the two approaches).
+For serine, the chromatographic peak is about 5 seconds wide. We thus adapt the
+`peakwidth` for the present data set and repeat the peak detection using these
+settings. In general, the lower and upper peak width should be set to include
+most of the chromatographic peak widths. For the present data set we set the
+values to 2 to 10 seconds, i.e., to about half and two times the expected peak
+width. In addition, by setting `integrate = 2`, we select a different peak
+boundary estimation algorithm. This works particularly well for non-gaussian
+peak shapes and ensures that also signal from the peak's tail is integrated
+(eventually re-run the code with the default `integrate = 1` to compare the two
+approaches).
 
 ```{r centWave-adapted, fig.cap = "EIC for Serine with detected chromatographic peak", results = "hide"}
 cwp <- CentWaveParam(peakwidth = c(2, 10), integrate = 2)
@@ -832,7 +833,10 @@ plot(serine_chr)
 
 Acceptable values for parameter `peakwidth` can thus be derived through visual
 inspection of EICs for ions known to be present in the sample (e.g. of internal
-standards). Ideally, this should be done for several compounds/ions.
+standards). Ideally, this should be done for several compounds/ions. Tip:
+ensure that the EIC contains also enough signal left and right of the actual
+chromatographic peak to allow *centWave* to properly estimate the background
+noise. Alternatively, reduce the value for the `snthresh` parameter.
 
 With our data set-specific `peakwidth` we were able to detect the peak for
 serine (highlighted in grey in the plot above). We can now use the `chromPeaks`
@@ -844,18 +848,18 @@ chromPeaks(serine_chr)
 ```
 
 The result is returned as a `matrix` with each row representing one identified
-chromatographic peak. The retention time and *m/z* ranges of the peaks are
-provided in columns `"rtmin"`, `"rtmax"`, `"mzmin"` and `"mzmax"`, the
-integrated peak area (i.e., the *abundance* of the ion) in column `"into"`, the
-maximal signal of the peak in column `"maxo"` and the signal to noise ratio in
-column`"sn"`. With our adapted settings we were thus able to identify a
-chromatographic peak for the serine ion in each of the two samples.
+chromatographic peak. The retention time ranges of the peaks are provided in
+columns `"rtmin"` and `"rtmax"`, the integrated peak area (i.e., the *abundance*
+of the ion) in column `"into"`, the maximal signal of the peak in column
+`"maxo"` and the signal to noise ratio in column`"sn"`. With our adapted
+settings we were thus able to identify a chromatographic peak for the serine ion
+in each of the two samples.
 
 The second important parameter for *centWave* is `ppm` which is used in the
 initial definition of the *regions of interest* (ROI) in which the actual peak
 detection is then performed. To define these ROI, the algorithm evaluates for
 each mass peak in a spectrum whether a mass peak with a similar *m/z* (and a
-reasonable high intensity) is also found in the following spectrum. For this,
+reasonably high intensity) is also found in the subsequent spectrum. For this,
 only mass peaks with a difference in their *m/z* smaller than `ppm` in
 consecutive scans are considered. To illustrate this, we plot again the full
 MS data for the data subset containing signal for serine.
@@ -926,29 +930,28 @@ data <- findChromPeaks(data, param = cwp)
 ```
 
 The results form the chromatographic peak detection were added by the
-`findChromPeaks` to our `data` variable which now is an
-`XcmsExperiment` object that, by extending the `MsExperiment` class inherits all
-of its functionality and properties, but in addition contains also all *xcms*
-preprocessing results.
+`findChromPeaks` to our `data` variable which now is an `XcmsExperiment` object
+that, by extending the `MsExperiment` class inherits all of its functionality
+and properties, but in addition contains also all *xcms* preprocessing results.
 
 ```{r}
 data
 ```
 
 We can extract the results from the peak detection step (as above) with the
-`chromPeaks` function. The optional parameters `rt` and `mz` would allow in
-addition to extract peak detection results for a specific *m/z* - retention time
-region. In our example we extract all chromatographic peaks between a *m/z*
-range from 106 to 108 and a retention time from 150 to 190.
+`chromPeaks` function. The optional parameters `rt` and `mz` would allow to
+extract peak detection results for a specified *m/z* - retention time region. In
+our example we extract all chromatographic peaks between an *m/z* range from 106
+to 108 and a retention time from 150 to 190.
 
-```{r xcmsnexp}
+```{r}
 #' Access the peak detection results from a specific m/z - rt area
 chromPeaks(data, mz = c(106, 108), rt = c(150, 190))
 ```
 
 Again, each row in this matrix contains one identified chromatographic peak with
 columns `"mz"`, `"mzmin"`, `"mzmax"`, `"rt"`, `"rtmin"` and `"rtmax"` defining
-it's *position* (and size) in the *m/z*-rt plane and `"into"` and `"maxo"` its
+it's *position* (and size) in the *m/z* - rt plane and `"into"` and `"maxo"` its
 (integrated and maximum) intensity. Column `"sample"` indicates in which of our
 samples (data files) the peak was identified.
 
@@ -963,18 +966,20 @@ determine (and eventually) plot the number of identified chromatographic peaks
 per sample. Below we count the number of peaks per sample.
 
 ```{r}
+#' Count peaks per file
 chromPeaks(data)[, "sample"] |>
     table()
 ```
 
 About the same number of peaks was identified, which is to be expected since
-both files contain measurements from the same QC sample pool.
+both files contain measurements from the same sample (the QC pool).
 
 As an additional visual quality assessment, we can also plot the location of the
-identified chromatographic peaks in the *m/z*-retention time space for each data
-file using the `plotChromPeaks` function.
+identified chromatographic peaks in the *m/z* - retention time space for each
+data file using the `plotChromPeaks` function.
 
 ```{r plotChromPeaks, fig.cap = "Location of the identified chromatographic peaks in the *m/z* - rt space.", fig.height = 7, fig.width = 12}
+#' Plot the location of peaks in the m/z - rt plane
 par(mfrow = c(1, 2))
 plotChromPeaks(data, 1)
 plotChromPeaks(data, 2)
@@ -985,10 +990,10 @@ Again, similar pattern are expected to be present for the two data files.
 After chromatographic peak detection it is generally a good idea to visually
 inspect individual chromatographic peaks to evaluate the performance of the peak
 detection step. This could be done by plotting EICs of known compounds/ions in
-the data or by randomly selected chromatographic peaks. To illustrate this we
+the data or of randomly selected chromatographic peaks. To illustrate this we
 define below a matrix with *m/z* and retention time ranges for a set of
-(pre)selected peaks. The same matrix could also be created for randomly selected
-peaks or it could also be defined manually. For our example we further expand
+(pre)selected peaks. The same matrix could also be created either by randomly
+selecting peaks or by defining it manually. For our example we further expand
 the retention time and *m/z* ranges of the peaks, extract the EICs with the
 `chromatogram` function and finally plot these. Identified chromatographic peaks
 within the plotted regions will by default be highlighted in a semitransparent
@@ -1014,20 +1019,23 @@ plot(eics)
 
 While the peak detection worked nicely for the signals in the upper row, it
 failed to define chromatographic peaks containing the full signal in the lower
-row. In both cases, the signal was split into separate chromatographic
-peaks. This is a common problem with *centWave* on noisy and broad signals. We
-could either try to adapt the *centWave* settings and repeat the chromatographic
-peak detection or use the `refineChromPeaks` function that allows to
-post-process peak detection results and fix problems such as those observed
-above (see also the documentation of the `refineChromPeaks` function for all
-possible refinement options). To fuse the wrongly split peaks in the second row,
-we use the `MergeNeighboringPeaksParam` algorithm and configure it to merge all
+row. In both cases, the signal was split into separate chromatographic peaks
+within the same sample. This is a common problem with *centWave* on noisy and
+broad signals. We could either try to adapt the *centWave* settings and repeat
+the chromatographic peak detection or use the `refineChromPeaks` function that
+allows to post-process peak detection results and fix problems such as those
+observed above (see also the documentation of the `refineChromPeaks` function
+for all possible refinement options).
+
+To fuse the wrongly split peaks in the second row, we use the
+`MergeNeighboringPeaksParam` algorithm and configure it to merge all
 chromatographic peaks with a similar *m/z* that are less than 8 seconds apart
-from each other on the retention time axis (parameter `expandRt = 4`, distance
-tail to head of the peaks should thus be less than `2 * expandRt`) and for which
-the signal (intensity) between the two peaks is higher than 75% of the smaller
-apex intensity of the two peaks (parameter `minProp = 0.75`). We below apply
-these settings on the EICs and evaluate the result of this post-processing.
+from each other on the retention time axis (parameter `expandRt = 4`; the
+distance tail to head of the peaks evaluated for merging should thus be less
+than `2 * expandRt`) and for which the signal (intensity) between the two peaks
+is higher than 75% of the smaller apex intensity of the two peaks (parameter
+`minProp = 0.75`). We below apply these settings on the EICs and evaluate the
+result of this post-processing.
 
 ```{r}
 #' Define the setting for the peak refinement
@@ -1042,14 +1050,16 @@ plot(eics)
 
 The peak post-processing was able to fuse the signal for the neighboring peaks
 in the lower panel, while keeping the peaks for the different isomers present in
-the top right plot separate. We thus next apply the peak refinement on the full
+the top right plot separate. We next apply this same peak refinement on the full
 data set.
 
 ```{r}
+#' Perform peak refinement
 data <- refineChromPeaks(data, param = mpp)
 ```
 
 
+LLLLLLLLL
 ## Alignment
 
 While chromatography helps to better discriminate between analytes it is also

From 6f478eb53bf86530952f2a3ce6fba3a561980a99 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Sat, 23 Sep 2023 16:03:38 +0200
Subject: [PATCH 04/42] Update alignment section

---
 vignettes/xcms-preprocessing.Rmd | 145 +++++++++++++++++--------------
 1 file changed, 79 insertions(+), 66 deletions(-)

diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index d4decae..6f79883 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -640,7 +640,7 @@ color coded. Below we subset the data to the *m/z* - retention time region
 containing signal for serine and visualize the full MS data measured for that
 region in both data files.
 
-```{r serine-profile-mode-data, fig.cap = "Profile data for Serine.", fig.width = 10, fig.height = 5, fig.pos = "h!"}
+```{r serine-profile-mode-data, fig.cap = "Profile data for Serine."}
 data |>
 filterRt(rt = c(175, 189)) |>
 filterMz(mz = c(106.02, 106.07)) |>
@@ -665,7 +665,7 @@ maximum signal for each mass peak in each spectrum. Finally, we replace the
 spectra in data (`MsExperiment`) object with the centroided spectra and
 visualize the result repeating the visualization from above.
 
-```{r centroiding, fig.cap = "Centroided data for Serine.", fig.width = 10, fig.height = 5, fig.pos = "h!"}
+```{r centroiding, fig.cap = "Centroided data for Serine."}
 #' Smooth and centroid the spectra data
 sps_cent <- spectra(data) |>
 smooth(method = "SavitzkyGolay", halfWindowSize = 6L) |>
@@ -978,7 +978,7 @@ As an additional visual quality assessment, we can also plot the location of the
 identified chromatographic peaks in the *m/z* - retention time space for each
 data file using the `plotChromPeaks` function.
 
-```{r plotChromPeaks, fig.cap = "Location of the identified chromatographic peaks in the *m/z* - rt space.", fig.height = 7, fig.width = 12}
+```{r plotChromPeaks, fig.cap = "Location of the identified chromatographic peaks in the *m/z* - rt space."}
 #' Plot the location of peaks in the m/z - rt plane
 par(mfrow = c(1, 2))
 plotChromPeaks(data, 1)
@@ -1059,7 +1059,7 @@ data <- refineChromPeaks(data, param = mpp)
 ```
 
 
-LLLLLLLLL
+
 ## Alignment
 
 While chromatography helps to better discriminate between analytes it is also
@@ -1070,15 +1070,15 @@ set. In the `chromatogram` call, we set the optional parameter `chromPeaks =
 "none"` to avoid the additional extraction of all identified chromatographic
 peaks.
 
-```{r alignment-bpc-raw, fig.cap = "BPC of all files.", fig.width = 8, fig.height = 4}
+```{r alignment-bpc-raw, fig.cap = "BPC of all files."}
 #' Extract base peak chromatograms
 bpc_raw <- chromatogram(data, aggregationFun = "max", chromPeaks = "none")
 plot(bpc_raw, peakType = "none")
 ```
 
-While both samples were measured with the same setup on the same day, slight
-drifts of the signal are visible. These were also already visible in the EIC for
-serine, that we plot again below.
+While both samples were measured with the same setup in the same measurement
+run, slight drifts of the signal are visible. These were also already visible in
+the EIC for serine, that we plot again below.
 
 ```{r}
 plot(serine_chr, xlim = c(175, 190))
@@ -1086,8 +1086,9 @@ grid()
 ```
 
 For the serine signal, there seems to be a retention time shift of about 1
-second. The alignment step aims to minimize these retention time differences
-between all samples within an experiment.
+second between the two samples. The alignment step aims to minimize these
+retention time differences between all samples within an experiment (see below
+for an illustration).
 
 ![Alignment](images/alignment.png)
 
@@ -1098,9 +1099,9 @@ with the respective parameter objects:
 - `PeakGroupsParam`: the *peakGroups* [@Smith:2006ic] method aligns samples
   based on the retention times of a set of so called *hook peaks* (or
   housekeeping peaks) in the different samples of an experiment. These peaks are
-  supposed to represent signal from ions expected to be present in most samples
-  and the method aligns the samples by minimizing the present differences
-  between the retention times of these peaks.
+  supposed to represent signal from ions expected to be present in most of the
+  samples of an experiment and the method aligns these samples by minimizing the
+  between-sample retention time differences observed for these peaks.
 
 - `ObiwarpParam`: *obiwarp* [@Prince:2006jj] performs retention time adjustment
   based on the full *m/z* - retention time data. See the documentation for
@@ -1113,27 +1114,28 @@ would also be possible to estimate them on a subset of samples, such as
 repeatedly measured QC samples (e.g. sample pools) and adjust the full
 experiment based on these. See the alignment section in the *xcms*
 [vignette](https://bioconductor.org/packages/release/bioc/vignettes/xcms/inst/doc/xcms.html)
-for more information on this subset-based alignment.
+for more information on this subset-based alignment. Note that such a
+subset-based alignment requires the samples to be loaded in the order in which
+they were measured.
 
 For our example we use the *peakGroups* method that, as mentioned above, aligns
 samples based on the retention times of *hook peaks* (or housekeeping peaks). To
-define these we need however to first group detected chromatographic peaks
-across samples using an initial correspondence analysis. Below we use the
-*peakDensity* method for correspondence. Details about this method and
-explanations on the choices of its parameters are provided in the next
-section. In brief, parameter `sampleGroups` defines to which sample group of the
-experiment individual samples belong to, and parameter `minFraction` specifies
-the proportion of samples (of one sample group) in which a chromatographic peak
-needs to be identified (for a particular *m/z* - retention time region) to group
-them into an LC-MS feature. For our example we use the sample group definition
-in `sampleData` of our `data` variable for parameter `sampleGroups` and set
-`minFraction = 1` requiring thus a chromatographic peak to be identified in both
-samples to define a feature. If a subset alignment is performed on
-e.g. repeatedly measured QC samples, the `minFraction` parameter could be set as
-high as 0.8 or 0.9 while otherwise lower values might be advisable. Since the
-aim of this initial correspondence is to define some (presumably well separated)
-groups of chromatographic peaks across the samples, its settings don't need to
-be fully optimized.
+define these, we need to first run an initial correspondence analysis to group
+chromatographic peaks across samples. Below we use the *peakDensity* method for
+correspondence. Details about this method and explanations on the choices of its
+parameters are provided in the next section. In brief, parameter `sampleGroups`
+defines to which sample group of the experiment individual samples belong to,
+and parameter `minFraction` specifies the proportion of samples (of one sample
+group) in which a chromatographic peak needs to be identified (for a particular
+*m/z* - retention time region) to group them into an LC-MS feature. For our
+example we use the sample group definition in the `sampleData` of our `data`
+variable and set `minFraction = 1` requiring thus a chromatographic peak to be
+identified in 100% of available samples to define a feature. Generally, if
+correspondence is performed on more heterogeneous samples `minFraction` values
+between 0.6 and 0.8 could be used instead. Since the aim of this initial
+correspondence is to define some (presumably well separated) groups of
+chromatographic peaks across the samples, its settings does not need to be fully
+optimized.
 
 ```{r}
 #' Define the settings for the initial peak grouping - details for
@@ -1145,16 +1147,23 @@ data <- groupChromPeaks(data, pdp)
 
 This step now grouped chromatographic peaks across samples and defined so called
 LC-MS features (or simply features). We can thus now run the alignment using the
-*peakGroups* algorithm. Parameter `minFraction` can be used to control the
-selection of the hook peaks. By setting `minFraction = 1` all features with
-identified chromatographic peaks in every sample will be used as hook peaks. The
-algorithm will minimize the between sample differences in retention time for all
-of these peaks. Parameter `span` defines the degree of smoothing of the loess
-function that is used to allow different regions along the retention time axis
-to be adjusted by a different factor. A value of 0 will most likely cause
-overfitting, while 1 would cause all retention times of a sample to be shifted
-by a constant value. Values between 0.4 and 0.6 seem to be reasonable for most
-experiments.
+*peakGroups* algorithm. The main parameter to define the hook peaks is (again)
+`minFraction`. Similar to the definition above, `minFraction` refers to the
+proportion of samples in which a chromatographic peak needs to be present. By
+setting `minFraction = 1` we base the alignment on features with peaks
+identified in 100% of the samples in the data set. For alignments that are
+based on repeatedly measured samples (e.g. also for subset-based alignment on
+sample pools) values `>= 0.9` can be used. Otherwise, values between 0.7 and 0.9
+might be more advisable to ensure that a reasonable set of features are
+selected.
+
+After having identified the features that should be used as *hook peaks* the
+algorithm minimizes the observed between-sample retention time differences for
+these. Parameter `span` defines the degree of smoothing of the loess function
+that is used to allow different regions along the retention time axis to be
+adjusted by a different factor. A value of 0 will most likely cause overfitting,
+while 1 would cause all retention times of a sample to be shifted by a constant
+value. Values between 0.4 and 0.6 seem to be reasonable for most experiments.
 
 ```{r alignment-correspondence}
 #' Define settings for the alignment
@@ -1162,22 +1171,16 @@ pgp <- PeakGroupsParam(minFraction = 1, span = 0.6)
 data <- adjustRtime(data, param = pgp)
 ```
 
-Alignment did not only adjust retention times of the individual (MS1) spectra of
-all files, but also adjusted the retention times of the identified
-chromatographic peaks, as well as the retention times of possibly present MS2
-spectra. The adjusted retention times are stored as a new spectra variable
-`"rtime_adjusted"` in the result object's `Spectra`. The `rtime` function on the
-result object will also by default return these values.
-
-After alignment it is suggested to evaluate its results using the
+After an alignment it is suggested to evaluate its results using the
 `plotAdjustedRtime` function. This function plots the differences between
 adjusted and raw retention times for each sample on the y-axis along the
-adjusted retention times on the x-axis. Points indicate the position of
+adjusted retention times on the x-axis (each line hence representing the
+retention time adjustment of one sample/file). Points indicate the position of
 individual hook peaks along the retention time axis, with a dotted line
 connecting the peaks belonging to the same feature (for which the algorithm
 minimized the difference in retention times).
 
-```{r alignment-result, fig.width = 8, fig.height = 4, fig.cap = "Alignment results."}
+```{r alignment-result, fig.cap = "Alignment results: differences between raw and adjusted retention times for each sample."}
 #' Plot the difference between raw and adjusted retention times
 plotAdjustedRtime(data)
 grid()
@@ -1186,11 +1189,11 @@ grid()
 As a rule of thumb, the differences between raw and adjusted retention times in
 the plot above should be reasonable. Also, if possible, hook peaks should be
 present along a wide span of the retention time range, to avoid the need for
-extrapolation (which usually results in a too strong adjustment).  For our
+extrapolation (which usually results in a too strong adjustment). For our
 example, the largest adjustments are between 1 and 2 seconds, which is
-reasonable given that the two samples were measured on the same day. Also,
-features used for the alignment (i.e. hook peaks) are spread across the full
-retention time range.
+reasonable given that the two samples were measured during the same measurement
+run. Also, features used for the alignment (i.e. hook peaks) are spread across
+the full retention time range.
 
 To evaluate the impact of the alignment we next also plot the BPC before and
 after alignment. In a similar way as before, we set `chromPeaks = "none"` in the
@@ -1198,7 +1201,7 @@ after alignment. In a similar way as before, we set `chromPeaks = "none"` in the
 chromatographic peaks in the returned chromatographic data.
 
 
-```{r bpc-raw-adjusted, fig.cap = "BPC before (top) and after (bottom) alignment.", fig.width = 10, fig.height = 8}
+```{r bpc-raw-adjusted, fig.cap = "BPC before (top) and after (bottom) alignment."}
 par(mfrow = c(2, 1))
 #' Plot the raw base peak chromatogram
 plot(bpc_raw)
@@ -1213,7 +1216,7 @@ adjustment. In addition to this general assessment, the alignment result should
 also be evaluated for selected compounds (or internal standards). We thus below
 plot the EIC for the *[M+H]+* ion for serine before and after alignment.
 
-```{r serine-xic-adjusted, fig.cap = "EIC for Serine before (left) and after (right) alignment", fig.width = 10, fig.height = 4}
+```{r serine-xic-adjusted, fig.cap = "EIC for Serine before (left) and after (right) alignment"}
 par(mfrow = c(1, 2), mar = c(4, 4.5, 1, 0.5))
 plot(serine_chr)
 grid()
@@ -1225,7 +1228,17 @@ plot(serine_chr_adj)
 grid()
 ```
 
-The serine peaks are also nicely aligned after retention time adjustment.
+The serine peaks are also nicely aligned after retention time adjustment. Again,
+it is advisable to evaluate the impact of the alignment on several EICs, ideally
+also spread along the retention time range.
+
+Note that `adjustRtime`, in addition to the retention times of the individual
+(MS1) spectra of all files, adjusted also the retention times of the identified
+chromatographic peaks, as well as retention times of possibly present MS2
+spectra. The adjusted retention times are stored as a new spectra variable
+`"rtime_adjusted"` in the result object's `Spectra`. The `rtime` function on the
+result object will by default return these (adjusted) values.
+
 
 
 ## Correspondence
@@ -1301,7 +1314,7 @@ multiple, not a single example slice. We thus below extract a chromatogram for
 an *m/z* slice containing signal for known isomers betaine and valine ([M+H]+
 *m/z* 118.08625).
 
-```{r correspondence-bw, fig.cap = "Correspondence analysis with default settings on an *m/z* slice containing signal from multiple ions.", fig.width = 10, fig.height = 7}
+```{r correspondence-bw, fig.cap = "Correspondence analysis with default settings on an *m/z* slice containing signal from multiple ions."}
 #' Plot the chromatogram for an m/z slice containing betaine and valine
 mzr <- 118.08625 + c(-0.01, 0.01)
 chr_2 <- chromatogram(data, mz = mzr, aggregationFun = "max")
@@ -1317,7 +1330,7 @@ feature (indicated with the grey rectangle). Signal from different ions would
 thus be treated as a single entity. We repeat the analysis below with a strongly
 reduced value for parameter `bw`.
 
-```{r correspondence-bw-fix, fig.cap = "Correspondence analysis with reduced bw setting on a *m/z* slice containing signal from multiple ions.", fig.width = 10, fig.height = 7}
+```{r correspondence-bw-fix, fig.cap = "Correspondence analysis with reduced bw setting on a *m/z* slice containing signal from multiple ions."}
 #' Reducing the bandwidth
 pdp <- PeakDensityParam(sampleGroups = sampleData(data)$group, bw = 1.8)
 plotChromPeakDensity(chr_2, param = pdp)
@@ -1362,7 +1375,7 @@ the results from the actual correspondence analysis (based also on the settings
 that were used) by setting `simulate = FALSE` in the `plotChromPeakDensity`
 call.
 
-```{r correspondence-evaluate, fig.cap = "Result of correspondence on a slice containing the isomers valine and betaine.", fig.width = 10, fig.heigt = 7}
+```{r correspondence-evaluate, fig.cap = "Result of correspondence on a slice containing the isomers valine and betaine."}
 #' Extract chromatogram including signal for betaine and valine
 chr_2 <- chromatogram(data, mz = 118.08625 + c(-0.01, 0.01),
                       aggregationFun = "max")
@@ -1373,7 +1386,7 @@ plotChromPeakDensity(chr_2, simulate = FALSE)
 We evaluate the results also on a different slice containing signal for ions
 from isomers leucine and isoleucine ([M+H]+ *m/z* 132.10191).
 
-```{r correspondence-evaluate-2, fig.cap = "Result of correspondence on a slice containing the isomers leucine and isoleucine.", fig.width = 10, fig.heigt = 7}
+```{r correspondence-evaluate-2, fig.cap = "Result of correspondence on a slice containing the isomers leucine and isoleucine."}
 #' Extract chromatogram with signal for isomers leucine and isoleucine
 chr_3 <- chromatogram(data, mz = 132.10191 + c(-0.01, 0.01),
                       aggregationFun = "max")
@@ -1448,7 +1461,7 @@ To illustrate this we below extract the *m/z* - retention time region for
 selected features, extract their EICs and plot them (using a different color for
 each sample).
 
-```{r gap-filling-example, fig.width = 10, fig.height = 8, fig.cap = "Examples of features for which a peak was only identified in one sample."}
+```{r gap-filling-example, fig.cap = "Examples of features for which a peak was only identified in one sample."}
 #' Extract the m/z-rt region for selected peaks
 mz_rt <- featureArea(data, features = c("FT026", "FT028", "FT246", "FT306"))
 mz_rt[, "rtmin"] <- mz_rt[, "rtmin"] - 2
@@ -1488,7 +1501,7 @@ sum(is.na(featureValues(data)))
 With `fillChromPeaks` we could thus *rescue* signal for all but 26
 features. Below we extract the EICs for these and visually inspect some of them.
 
-```{r gap-filling-missing, fig.width = 10, fig.height = 10, fig.cap = "Features with missing values even after gap-filling."}
+```{r gap-filling-missing, fig.cap = "Features with missing values even after gap-filling."}
 #' Identify features with missing values
 fts <- rownames(featureValues(data))[is.na(rowSums(featureValues(data)))]
 
@@ -1512,7 +1525,7 @@ chromatographic peak was detected.
 In contrast, for the 4 example features from above a signal was *rescued*. Below
 we visualize the gap-filled chromatographic peaks for these.
 
-```{r gap-filling-example-2, fig.width = 10, fig.height = 8}
+```{r gap-filling-example-2}
 #' Extract the m/z-rt region for selected peaks
 mz_rt <- featureArea(data, features = c("FT026", "FT028", "FT246", "FT306"))
 mz_rt[, "rtmin"] <- mz_rt[, "rtmin"] - 2
@@ -1535,7 +1548,7 @@ replaced with `NA`. Finally, after calculating the row averages for both
 matrices (excluding missing values), the values are plotted against each
 other.
 
-```{r comparison-detected-filled, fig.width = 8, fig.height = 8, fig.cap = "Detected (x-axis) against filled (y-axis) signal. The black solid line represents the identity line."}
+```{r comparison-detected-filled, fig.cap = "Detected (x-axis) against filled (y-axis) signal. The black solid line represents the identity line."}
 #' Define matrices with detected or filled-in feature values
 vals_detect <- featureValues(data, filled = FALSE)
 vals_filled <- featureValues(data)

From f776c6289badb3d2783dbb206e08e8b0f942316f Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Sat, 23 Sep 2023 19:59:03 +0200
Subject: [PATCH 05/42] Update correspondence, gap filling and final results

---
 vignettes/xcms-preprocessing.Rmd | 273 +++++++++++++++++--------------
 1 file changed, 147 insertions(+), 126 deletions(-)

diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index 6f79883..f2dccac 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -1245,7 +1245,7 @@ result object will by default return these (adjusted) values.
 
 The final step of the LC-MS preprocessing with *xcms* is the correspondence
 analysis, in which chromatographic peaks from the same types of ions (compounds)
-are grouped across samples to form a so called *LC-MS features*.
+are grouped across samples to form the so called *LC-MS features*.
 
 ![Correspondence](images/correspondence2_03.png)
 
@@ -1272,16 +1272,17 @@ similar retention time will result in a higher peak density at a certain
 retention time and are thus grouped together. The grouping depends on the
 *smoothness* of the density curve and can be configured with parameter `bw`.
 
-An illustration of this is shown in the sketch below.
+An illustration how chromatographic peaks within a small *m/z* range are grouped
+by the *peakDensity* method is shown in the sketch below.
 
 ![peak density](images/correspondence2_density.png)
 
-Settings for this type correspondence algorithm can be tested and evaluated in
-EICs. We thus below extract a chromatgram for a *m/z* slice containing signal
-for a *[M+H]+* ion of serine and evaluate the correspondence on that slice using
-the `plotChromPeakDensity` function with default parameters for the
-*peakDensity* method and using the sample group assignment defined in
-`sampleData`.
+Settings for this algorithm can be best tested and optimized using the
+`plotChromPeakDensity` function on extracted chromatograms. We below extract a
+chromatogram for a *m/z* slice containing signal for a *[M+H]+* ion of serine
+and evaluate the result from a *peakDensity* correspondence analysis using that
+function. We use the default settings (`bw = 30`) and use again the sample group
+assignment defined in `sampleData`.
 
 ```{r}
 #' Extract a chromatogram for a m/z range containing serine
@@ -1294,25 +1295,31 @@ pdp <- PeakDensityParam(sampleGroups = sampleData(data)$group, bw = 30)
 plotChromPeakDensity(chr_1, param = pdp)
 ```
 
-The upper panel in the plot above shows the chromatographic data with the
-identified peaks. The lower panel plots the retention time of identified
-chromatographic peaks on the x-axis against the index of the sample in which the
-peak was identified. Each chromatographic peak is thus indicated with a point in
-that plot. In our example there was one chromatographic peak identified in each
-sample at a retention time of about 180 seconds and these two peaks are thus
-shown. The black solid line represents the density estimation (distribution) of
-the identified chromatographic peaks along the retention time. The smoothness of
-this curve (which is created with the base R `density` function) is configured
-with the parameter `bw`. The *peakDensity* algorithm assigns all chromatographic
-peaks within the same *peak* of this density estimation curve to the same
-feature. Chromatographic peaks assigned to the same feature are indicated with a
-grey rectangle in that plot (for the present example, because retention times of
-the two chromatographic peaks are very similar, this rectangle is very narrow
-and looks more like a vertical line). The default settings (`bw = 30`) thus seem
-to correctly define features. It is however advisable to evaluate settings on
-multiple, not a single example slice. We thus below extract a chromatogram for
-an *m/z* slice containing signal for known isomers betaine and valine ([M+H]+
-*m/z* 118.08625).
+The upper panel in the plot shows the chromatographic data for the selected
+*m/z* slice with the identified peaks highlighted in grey. The lower panel plots
+the retention time of identified chromatographic peaks on the x-axis against the
+index of the sample in which the peak was identified. Each chromatographic peak
+is thus represented with a point in that plot (x-axis value being its retention
+time and the y-axis value the sample in which it was identified). In our example
+there was one chromatographic peak identified in each sample at a retention time
+of about 180 seconds and these two peaks are thus shown. The black solid line
+represents the density estimation (i.e. distribution or retention times) of the
+identified chromatographic peaks along the retention time axis. The smoothness
+of this curve (which is created with the base R `density` function) is
+configured with the parameter `bw`. The *peakDensity* algorithm assigns all
+chromatographic peaks within the same *peak* of this density estimation curve to
+the same feature. Chromatographic peaks assigned to the same feature are
+indicated with a grey rectangle in the plot. In the present example, because
+retention times of the two chromatographic peaks are very similar, this
+rectangle is very narrow and looks thus more like a vertical line. Based on this
+result, the default settings (`bw = 30`) seemed to correctly define features. It
+is however advisable to evaluate settings on multiple slices, ideally with
+signal from more than one compound being present. Such slices could be
+identified in e.g. a plot created with the `plotChromPeaks` function (see
+example in the chromatographic peak detection section).
+
+In our example we extract a chromatogram for an *m/z* slice containing signal
+for known isomers betaine and valine ([M+H]+ *m/z* 118.08625).
 
 ```{r correspondence-bw, fig.cap = "Correspondence analysis with default settings on an *m/z* slice containing signal from multiple ions."}
 #' Plot the chromatogram for an m/z slice containing betaine and valine
@@ -1325,7 +1332,7 @@ plotChromPeakDensity(chr_2, param = pdp)
 
 This slice contains signal from several ions resulting in multiple
 chromatographic peaks along the retention time axis. With the default settings,
-in particular `bw = 30`, all these peaks were however assigned to the same
+in particular with `bw = 30`, all these peaks were however assigned to the same
 feature (indicated with the grey rectangle). Signal from different ions would
 thus be treated as a single entity. We repeat the analysis below with a strongly
 reduced value for parameter `bw`.
@@ -1336,28 +1343,29 @@ pdp <- PeakDensityParam(sampleGroups = sampleData(data)$group, bw = 1.8)
 plotChromPeakDensity(chr_2, param = pdp)
 ```
 
-The `bw = 1.8` strongly reduced the smoothness of the density curve resulting in
-a higher number of density *peaks* and hence a nice grouping of (aligned)
-chromatographic peaks into separate features. Note that the height of the peaks
-of the density curve are not considered for the grouping. By having defined a
-`bw` appropriate for our data set we proceed and perform the correspondence
-analysis on the full data set. Other parameters of *peakDensity* are `binSize`
-and `minFraction`. The former defines the *m/z* widths of the slices along the
-*m/z* dimension the algorithm will iterate through. This value depends on the
-resolution (and noise) of the instrument, and should not be set to a too small
-value, but also not too large (to avoid peaks from different ions, with slightly
-different *m/z* but similar retention times, to be grouped into the same
-feature). The `minFraction` parameter (already discussed above) defines the
-proportion of samples within at least one sample group in which chromatographic
-peaks need to be identified in order to define a feature. For our example we use
-a `binSize = 0.02` hence grouping chromatographic peaks, with similar retention
-time, and with a difference in their *m/z* which is smaller than 0.02 into the
-same feature and `minFraction = 0.4` thus features will be defined if a
-chromatographic peak was identified in 50% of the samples (hence in at least on
-for the present data set).
+Setting `bw = 1.8` strongly reduced the smoothness of the density curve
+resulting in a higher number of density *peaks* and hence a nice grouping of
+(aligned) chromatographic peaks into separate features. Note that the height of
+the peaks of the density curve are not considered for the grouping.
+
+By having defined a `bw` appropriate for our data set, we proceed and perform
+the correspondence analysis on the full data set. Other parameters of
+*peakDensity* are `binSize` and `minFraction`. The former defines the *m/z*
+widths of the slices along the *m/z* dimension the algorithm will iterate
+through. This value depends on the resolution (and noise) of the instrument, and
+should not be set to a too small value, but also not too large (to avoid peaks
+from different ions, with slightly different *m/z* but similar retention times,
+to be grouped into the same feature). The `minFraction` parameter (already
+discussed above) defines the proportion of samples within at least one sample
+group in which chromatographic peaks need to be identified in order to define a
+feature. For our example we use a `binSize = 0.02` hence grouping
+chromatographic peaks, with similar retention time, and with a difference in
+their *m/z* that is smaller than 0.02 into the same feature and `minFraction =
+0.4` thus defining features for chromatographic peak that were identified in at
+least 50% of samples per sample group.
 
 ```{r correspondence-analysis}
-#' Define settings
+#' Set optimized settings
 pdp <- PeakDensityParam(sampleGroups = sampleData(data)$group, bw = 1.8,
                         minFraction = 0.4, binSize = 0.02)
 
@@ -1366,19 +1374,19 @@ data <- groupChromPeaks(data, param = pdp)
 data
 ```
 
-Over 300 features were thus identified in the present data set. Again, it is
-suggested to evaluate the results again for selected compounds/ions. We thus
-extract below again a chromatogram for an *m/z* range containing signals for
-betaine and valine (and several additional ions). After a correspondence
-analysis also features are extracted by the `chromatogram` call and we can show
-the results from the actual correspondence analysis (based also on the settings
-that were used) by setting `simulate = FALSE` in the `plotChromPeakDensity`
-call.
+Over 300 features were identified in our example data set. Again, it is
+suggested to evaluate the results on selected compounds/ions. We therefore
+extract below the chromatogram for the *m/z* range containing signals for
+betaine and valine. After a correspondence analysis also features are extracted
+by the `chromatogram` call and we can show the results from the actual
+correspondence analysis (based also on the settings that were used) by setting
+`simulate = FALSE` in the `plotChromPeakDensity` call.
 
 ```{r correspondence-evaluate, fig.cap = "Result of correspondence on a slice containing the isomers valine and betaine."}
 #' Extract chromatogram including signal for betaine and valine
 chr_2 <- chromatogram(data, mz = 118.08625 + c(-0.01, 0.01),
                       aggregationFun = "max")
+#' Setting simulate = FALSE to show the actual correspondence results
 plotChromPeakDensity(chr_2, simulate = FALSE)
 
 ```
@@ -1396,22 +1404,21 @@ plotChromPeakDensity(chr_3, simulate = FALSE)
 Despite being very close, chromatographic peaks of the two isomers were
 successfully grouped into separate features. Even the partially overlapping
 signal from a third ion eluting at about the same time and hence partially
-overlapping with the peak at a retention time of about 152 seconds was grouped
-into its own feature. It is at the discretion of the data analysts to define how
-fine or coarse the feature grouping should be. Especially for larger
+overlapping with the peak at a retention time of 152 seconds was grouped into
+its own, separate, feature. It is at the discretion of the data analysts to
+define how fine or coarse the feature grouping should be. Especially for larger
 experiments, with more samples and also larger variation in retention time it
 might not always be possible to completely separate all closely eluting ions
 from each other and sometimes it might be acceptable to group them into a single
-feature (keeping in mind that it could however represent signal from different
-ions/compounds).
+feature (keeping in mind that this feature would then however represent signal
+from different ions/compounds).
 
 Similar to the peak detection and alignment results, also the results from the
-correspondence analysis were added to the `XcmsExperiment` object. These
-can be extracted with the `featureDefinitions` function, that extracts the
-*definition* of the LC-MS features and the `featureValues` function that
-extracts the numerical matrix with the feature abundances (in all
-samples). Below we extract the definition of the features and display the first
-6 rows
+correspondence analysis were added to the `XcmsExperiment` object. These can be
+extracted with the `featureDefinitions` function, that extracts the *definition*
+of the LC-MS features and the `featureValues` function that extracts the
+numerical matrix with the feature abundances (in all samples). Below we extract
+the definition of the features and display the first 6 rows
 
 ```{r correspondence-featureDefinitions}
 #' Definition of the features
@@ -1423,45 +1430,50 @@ Each row defines one feature and provides information on it's *m/z* (column
 number of chromatographic peaks that were assigned to the feature and the MS
 level. Column `"peakidx"` provides the indices of the chromatographic peaks in
 the `chromPeaks` matrix that were assigned to the feature - but generally users
-will not need or extract that information. The feature abundance matrix, which
-is the final result of the *xcms* preprocessing, can be extracted with the
-`featureValues` function. By default, with parameter `method = "maxint"`, it
-returns for each feature the integrated peak signal of the chromatographic peak
-with the highest signal per sample. Note that this has only an effect for
-features with more than one chromatographic peak per sample (i.e., if multiple
-chromatographic peaks in the **same** sample were grouped into the same feature
-because of their closeness in retention time). An alternative to the above
-mentioned default would be to set `method = "sum"` that would sum the
-intensities for such multi-peak features. Below we extract the feature
+will not need or extract that information.
+
+The feature abundance matrix, which is the final result of the *xcms*
+preprocessing, can be extracted with the `featureValues` function. By default,
+with parameter `method = "maxint"`, it returns for each feature the integrated
+peak signal of the chromatographic peak with the highest signal per sample. Note
+that this has only an effect for features with more than one chromatographic
+peak per sample (i.e., if multiple chromatographic peaks in the **same** sample
+were grouped into the feature because of their closeness in retention
+time). Setting `method = "sum"` would in contrast sum the abundances of such
+chromatographic peaks. Note that `method = "sum"` is only suggested if, like in
+our example, neighboring and overlapping peaks per sample were merged to avoid
+an overestimation of the feature abundance. Below we extract the feature
 abundances and show the first 6 rows.
 
+
 ```{r}
+#' Get abundances for the first 6 features
 featureValues(data, method = "sum") |> head()
 ```
 
 We could now use this feature matrix for any downstream analysis. Such feature
-matrix might however, as can also be seen for the second row (feature *FT002*),
-contain missing values. These represent features for which no chromatographic
-peak was identified in one (or more) sample(s). While a number of methods exist
-to deal with such missing values, it might be sometimes more advisable to
-*rescue* signal instead of imputing it. This will be explained in the next
-section.
+matrix might however, as can also be seen in the second row above (feature
+*FT002*), contain missing values. These represent features for which no
+chromatographic peak was identified in one (or more) sample(s). While a number
+of imputation methods exist to deal with missing values, it might be more
+advisable to instead *rescue* signal. *xcms* provides such *gap filling* which
+is explained in the next section.
 
 
 ## Gap filling
 
 Missing values in feature matrices from an *xcms*-based preprocessing represent
 cases in which, in a particular sample, no chromatographic peak was identified
-in the *m/z* - retention time region of the feature. This could happen because
-the ion might simply not present in that sample, or because the peak
-detection algorithm failed to identify a chromatographic peak in that sample,
-either because the signal was too noisy, or too low (or a combination of both).
+in the *m/z* - retention time region of the feature. This could either represent
+a truly missing value (because the ion/compound was not present in that sample)
+or a failure of the peak detection algorithm to identify a peak (either because
+the measured signal was too noisy, or too low, or a combination of both).
 
 To illustrate this we below extract the *m/z* - retention time region for
 selected features, extract their EICs and plot them (using a different color for
 each sample).
 
-```{r gap-filling-example, fig.cap = "Examples of features for which a peak was only identified in one sample."}
+```{r features-missing-values, fig.cap = "Examples of features for which a peak was only identified in one sample."}
 #' Extract the m/z-rt region for selected peaks
 mz_rt <- featureArea(data, features = c("FT026", "FT028", "FT246", "FT306"))
 mz_rt[, "rtmin"] <- mz_rt[, "rtmin"] - 2
@@ -1473,18 +1485,20 @@ chrs <- chromatogram(data, mz = mz_rt[, c("mzmin", "mzmax")],
 plot(chrs, col = c("red", "blue"), lwd = 2)
 ```
 
-For all these feature, chromatographic peak detection failed to identify a peak
-in one of the two samples. For the features in the upper panel, the signal was
-most likely too low, while for the bottom left feature the signal was likely too
-noisy, and for the bottom right too sparse (i.e. to few data points to properly
-detect a peak). In all cases, however, some signal from presumably the same ion
-was measured in both samples, but in only one of the two a peak was
-identified. The aim of the *gap filling* is now to rescue signal for these
-features by integrating the intensities measured within the feature's *m/z* -
-retention time area in the sample in which no chromatographic peak was
-detected. In *xcms* this can be done with the `fillChromPeaks` function and the
-`ChromPeakAreaParam` to configure this step. Below we perform this gap filling
-showing also the number of missing values before and after running
+Indeed, for all these feature, chromatographic peak detection failed to identify
+a peak in one of the two samples. For the features in the upper panel, the
+signal was most likely too low, while for the bottom left feature the signal was
+likely too noisy, and for the bottom right too sparse (i.e. to few data points
+to properly detect a peak). In all cases, however, signal from (presumably)
+the same ion was measured in both samples. Thus, reporting a missing value would
+not be correct for these cases.
+
+The aim of the *gap filling* is now to rescue signal for such features by
+integrating the intensities measured within the feature's *m/z* - retention time
+area in the sample(s) in which no chromatographic peak was detected. In *xcms*
+this can be done with the `fillChromPeaks` function and the `ChromPeakAreaParam`
+which allows to configure this algorithm. Below we perform this gap filling
+summing also the number of missing values before and after running
 `fillChromPeaks`.
 
 ```{r fillChromPeaks}
@@ -1494,7 +1508,7 @@ sum(is.na(featureValues(data)))
 #' Perform gap filling
 data <- fillChromPeaks(data, param = ChromPeakAreaParam())
 
-#' How many missing values after
+#' Number of missing values after gap filling
 sum(is.na(featureValues(data)))
 ```
 
@@ -1525,7 +1539,7 @@ chromatographic peak was detected.
 In contrast, for the 4 example features from above a signal was *rescued*. Below
 we visualize the gap-filled chromatographic peaks for these.
 
-```{r gap-filling-example-2}
+```{r features-missing-values-filled}
 #' Extract the m/z-rt region for selected peaks
 mz_rt <- featureArea(data, features = c("FT026", "FT028", "FT246", "FT306"))
 mz_rt[, "rtmin"] <- mz_rt[, "rtmin"] - 2
@@ -1537,21 +1551,24 @@ chrs <- chromatogram(data, mz = mz_rt[, c("mzmin", "mzmax")],
 plot(chrs, col = c("red", "blue"), lwd = 2)
 ```
 
-A nice way to check that gap-filling was able to correctly *rescue* signals is
-to plot, for features with at least one missing value, the average detected
-against the average filled-in signal. Ideally, this should be done on QC samples
-or other repeatedly measured samples were no difference in abundance between
-features is expected. The code below extracts first only the detected feature
-values (by setting `filled = FALSE` in the `featureValues` call), then the
-detected and filled-in signal in which the detected signal is subsequently
-replaced with `NA`. Finally, after calculating the row averages for both
-matrices (excluding missing values), the values are plotted against each
-other.
+A nice way to check that gap-filling was able to correctly rescue signals is to
+plot, for features with at least one missing value, the average detected against
+the average filled-in signal. Ideally, this should be done on QC samples or
+other repeatedly measured samples were no difference in feature abundance
+between samples is expected.
+
+The code below extracts first only the detected feature values (by setting
+`filled = FALSE` in the `featureValues` call), then the detected **and**
+filled-in signal in which the detected signal is subsequently replaced with
+`NA`. Finally, after calculating the row averages for both matrices (excluding
+missing values), the values are plotted against each other.
 
 ```{r comparison-detected-filled, fig.cap = "Detected (x-axis) against filled (y-axis) signal. The black solid line represents the identity line."}
-#' Define matrices with detected or filled-in feature values
+#' Get only detected signal
 vals_detect <- featureValues(data, filled = FALSE)
+#' Get detected and filled-in signal
 vals_filled <- featureValues(data)
+#' Replace detected signal with NA
 vals_filled[!is.na(vals_detect)] <- NA
 
 #' Identify features with at least one filled peak
@@ -1568,14 +1585,15 @@ avg_filled <- avg_filled[has_filled]
 #' plot the values against each other (in log2 scale)
 plot(log2(avg_detect), log2(avg_filled),
      xlim = range(log2(c(avg_detect, avg_filled)), na.rm = TRUE),
-     ylim = range(log2(c(avg_detect, avg_filled)), na.rm = TRUE))
+     ylim = range(log2(c(avg_detect, avg_filled)), na.rm = TRUE),
+     pch = 21, bg = "#00000080")
 grid()
 abline(0, 1)
 ```
 
 As expected, detected signal is generally higher than filled-in signal. For the
 biggest part (in particular for higher intensities), filled-in and detected
-feature values are similar suggesting that the gap-filling step indeed rescued
+feature values are similar suggesting that the gap filling step indeed rescued
 signal.
 
 
@@ -1583,10 +1601,11 @@ signal.
 
 The *xcms* preprocessing results are all stored within an `XcmsExperiment`
 object. This includes the identified chromatographic peaks, the alignment
-results as well as the correspondence results. In addition, this result object
-keeps track of all performed processing steps and contains also the individual
-parameter objects used in the various preprocessing results. These can be
-extracted with the `processHistory` function:
+results as well as the correspondence results. In addition, to guarantee
+reproducibility, this result object keeps track of all performed processing
+steps and contains also the individual parameter objects used in the various
+preprocessing steps. These can be extracted with the `processHistory`
+function:
 
 ```{r}
 #' Process history
@@ -1600,15 +1619,18 @@ An individual parameter object can be extracted as follows:
 processHistory(data)[[1]]@param
 ```
 
+Thus, the used preprocessing algorithms along with all their settings are
+reported along the preprocessing results.
+
 As described above, values for the individual features can be extracted from the
 result object with the `featureValues` function and the definition of the
 features (which could be used for an initial annotation of the features based on
 their *m/z* and/or retention times) using the `featureDefinitions` function. In
 addition, the `XcmsExperiment` result object, through the internal `Spectra`
 object, keeps a *link* to the full MS data used for the analysis. For downstream
-analyses, that don't need access to these MS data anymore the preprocessing
+analyses, that don't need access to this MS data anymore the preprocessing
 results could be represented equally well using a `SummarizedExperiment` object,
-which is Bioconductor's standard container for large scale omics data. *xcms*
+which is Bioconductor's standard container for large-scale omics data. *xcms*
 provides with the `quantify` function a convenience function to extract all
 results from an `XcmsExperiment` result object and return it as a
 `SummarizedExperiment`. This function takes the same parameters than the
@@ -1641,7 +1663,6 @@ assay(res) |> head()
 ```
 
 
-
 ## What next?
 
 After preprocessing, the data could be normalized or scaled to remove any
@@ -1657,11 +1678,11 @@ available in R.
 As mentioned above, many chromatographic peaks (and subsequently also features)
 in untargeted metabolomics data sets will represent isotopes or also different
 ions/adducts of the same compound. The `r Biocpkg("CAMERA")` package aimed to
-identify and group such features in a data set. Such feature grouping
+identify and group such features in a data set. A similar feature grouping
 (compounding) can also be done for preprocessing results from newer versions of
-*xcms* using the `r Biocpkg("MsFeatures")` package, that enables grouping of
-features through a variety of different properties. See also the [feature
-grouping
+*xcms* using the `r Biocpkg("MsFeatures")` package. This package enables
+grouping of features through a variety of different methods. See also the
+[feature grouping
 vignette](https://sneumann.github.io/xcms/articles/LC-MS-feature-grouping.html)
 in *xcms* for more details.
 

From 3a56cf61a0dc49ada0d62f46649928f3feb3a2ea Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Mon, 25 Sep 2023 12:07:43 +0200
Subject: [PATCH 06/42] Address Phili's comments

---
 vignettes/xcms-preprocessing.Rmd | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index f2dccac..9cf44a3 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -1272,8 +1272,8 @@ similar retention time will result in a higher peak density at a certain
 retention time and are thus grouped together. The grouping depends on the
 *smoothness* of the density curve and can be configured with parameter `bw`.
 
-An illustration how chromatographic peaks within a small *m/z* range are grouped
-by the *peakDensity* method is shown in the sketch below.
+An illustration showing how chromatographic peaks within a small *m/z* range are
+grouped by the *peakDensity* method is shown in the sketch below.
 
 ![peak density](images/correspondence2_density.png)
 

From 232df9242cafa088d7b0bdd2cc8369a024a6c8d6 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Tue, 26 Sep 2023 10:30:10 +0200
Subject: [PATCH 07/42] Update Dockerfile

---
 Dockerfile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Dockerfile b/Dockerfile
index 2ec977b..b16cce6 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -10,7 +10,7 @@ WORKDIR /home/rstudio
 
 COPY --chown=rstudio:rstudio . /home/rstudio/
 
-## Install the SpectraTutorials package and additional required packages
-RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); BiocManager::install(ask = FALSE); BiocManager::install('RforMassSpectrometry/MsBackendMassbank'); BiocManager::install('mzR')"
+## Install the xcmsTutorials package and additional required packages
+RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); BiocManager::install(ask = FALSE)"
 
 RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); devtools::install('.', dependencies = TRUE, build_vignettes = TRUE, repos = BiocManager::repositories())"

From fe83cc93fae779e4007f65212bcc49de746d3503 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Tue, 26 Sep 2023 11:15:03 +0200
Subject: [PATCH 08/42] Update to dockerfile and small changes to the vignette

---
 Dockerfile                       |  2 +-
 vignettes/xcms-preprocessing.Rmd | 74 +++++++++++++++++++++++++++++---
 2 files changed, 68 insertions(+), 8 deletions(-)

diff --git a/Dockerfile b/Dockerfile
index b16cce6..2ac0117 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -11,6 +11,6 @@ WORKDIR /home/rstudio
 COPY --chown=rstudio:rstudio . /home/rstudio/
 
 ## Install the xcmsTutorials package and additional required packages
-RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); BiocManager::install(ask = FALSE)"
+RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); BiocManager::install(ask = FALSE); BiocManager::install('sneumann/xcms')"
 
 RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); devtools::install('.', dependencies = TRUE, build_vignettes = TRUE, repos = BiocManager::repositories())"
diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index 9cf44a3..5776dd5 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -23,6 +23,7 @@ bibliography: references.bib
 library("BiocStyle")
 library("knitr")
 library("rmarkdown")
+set.seed(123)
 opts_chunk$set(message = FALSE, error = FALSE, warning = FALSE,
                cache = FALSE, fig.width = 10, fig.height = 7)
 ```
@@ -1539,7 +1540,7 @@ chromatographic peak was detected.
 In contrast, for the 4 example features from above a signal was *rescued*. Below
 we visualize the gap-filled chromatographic peaks for these.
 
-```{r features-missing-values-filled}
+```{r features-missing-values-filled, fig.cap = "Features with filled-in signal."}
 #' Extract the m/z-rt region for selected peaks
 mz_rt <- featureArea(data, features = c("FT026", "FT028", "FT246", "FT306"))
 mz_rt[, "rtmin"] <- mz_rt[, "rtmin"] - 2
@@ -1706,23 +1707,82 @@ workshops/tutorials at
 
 # Final words
 
-- don't use defaults of `xcms`.
+- don't use defaults of *xcms*.
 - use the infrastructure to inspect and summarize the data.
-- don't limit yourself to just `xcms`, use the vast of additional packages,
-  methods, visualizations in R.
+- don't limit yourself to just *xcms*, use the vast number of additional
+  packages, methods, visualizations in R.
 - build custom workflows, data visualization approaches or your own analysis
   method.
 
+
 # Appendix
 
 ## Additional visualizations
 
 Visualization is key to understand the signal measured by the MS instrument and
-also to evaluate the performance (and quality) of the . LC-MS data is
+also to evaluate the performance (and quality) of a preprocessing. LC-MS data is
 particularly difficult to visualize because its 3-dimensional nature. Focusing
 on specific *m/z* retention time ranges (EICs) can help. In this section we
-provide some additional possibilities how such data can be visualized. In
-particular, we show how multiple EICs can be combined in a single plot.
+provide some additional examples how such data can be visualized. In particular,
+we show how multiple EICs can be combined in a single plot.
+
+```{r}
+data_1 <- data[1]
+plotChromPeaks(data)
+
+```
+
+- same data, just "3 dimensional"
+
+```{r}
+#' Full data for rt range
+cpks <- chromPeaks(data_1, rt = c(20, 220), type = "any")
+
+chrs_1 <- chromatogram(data_1, mz = cpks[, c("mzmin", "mzmax")],
+                       rt = cbind(rep(20, nrow(cpks)),
+                                  rep(220, nrow(cpks))))
+
+y <- plotChromatogramsOverlay(chrs_1, stacked = 1, bty = "n",
+                              col = "#00000020", peakCol = "#00000020",
+                              peakBg = "#00000020")
+
+#' focus on an area, where peaks seem "darker"
+abline(v = c(130, 150), lty = 3, col = "#ff000080")
+chrs_1s <- filterRt(chrs_1, rt = c(130, 150))
+
+y <- plotChromatogramsOverlay(chrs_1s, stacked = 1, bty = "n",
+                              col = "#00000020", peakCol = "#00000020",
+                              peakBg = "#00000020")
+
+#' Focus on an area with "darker" peaks.
+plotChromPeaks(data_1)
+rect(125, 113, 145, 119, lty = 3, border = "#ff000080")
+
+cpks <- chromPeaks(data_1, rt = c(125, 145), mz = c(113, 119))
+chrs_1s <- chromatogram(data_1, mz = cpks[, c("mzmin", "mzmax")],
+                        rt = cbind(rep(125, nrow(cpks)),
+                                   rep(145, nrow(cpks))))
+y <- plotChromatogramsOverlay(chrs_1s, stacked = 1, bty = "n",
+                              col = "#00000020", peakCol = "#00000020",
+                              peakBg = "#00000020")
+abline(h = y[[1]], col = "grey")
+mzs <- format(rowMeans(mz(chrs_1s)), digits = 5)
+text(x = rep(126, length(mzs)), y = y[[1]],
+     labels = paste0("m/z = ", mzs), pos = 1)
+
+
+#' Focus on a whole rt range
+plotChromPeaks(data_1)
+abline(v = c(185, 210), lty = 3, col = "#ff000080")
+
+chrs_1s <- filterRt(chrs_1, rt = c(185, 210))
+y <- plotChromatogramsOverlay(chrs_1s, stacked = 1, bty = "n",
+                              col = "#00000020", peakCol = "#00000020",
+                              peakBg = "#00000020")
+
+
+
+```
 
 TODO: continue here:
 

From a8509b0da9b99236f5520b175df5d7699033db0a Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Tue, 26 Sep 2023 13:23:58 +0200
Subject: [PATCH 09/42] Small fixes

---
 .github/workflows/build_deploy.yaml | 2 --
 vignettes/xcms-preprocessing.Rmd    | 4 ++--
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/build_deploy.yaml b/.github/workflows/build_deploy.yaml
index 642bdc4..8161cab 100644
--- a/.github/workflows/build_deploy.yaml
+++ b/.github/workflows/build_deploy.yaml
@@ -29,7 +29,6 @@ jobs:
         env:
           _R_CHECK_CRAN_INCOMING_REMOTE_: false
         run: |
-          service mariadb start
           Rscript -e "rcmdcheck::rcmdcheck(args = c('--no-manual'), error_on = 'error', check_dir = 'check')"
         shell: bash {0}
 
@@ -54,7 +53,6 @@ jobs:
           git config --global user.email "action@github.com"
           git config --global user.name "GitHub Action"
           git config --global --add safe.directory /__w/xcmsTutorials/xcmsTutorials
-          service mariadb start
           Rscript -e "pkgdown::deploy_to_branch(new_process = FALSE)"
         shell: bash {0}
         ## Note that you need to run pkgdown::deploy_to_branch(new_process = FALSE)
diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index 5776dd5..2dab64f 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -1,5 +1,5 @@
 ---
-title: "Exploring and Analyzing LC-MS data with *Spectra* and *xcms*"
+title: "Exploring and Analyzing LC-MS data with Spectra and xcms"
 author:
 - name: "Philippine Louail, Johannes Rainer"
   affiliation: "Eurac Research, Bolzano, Italy; johannes.rainer@eurac.edu github: jorainer"
@@ -11,7 +11,7 @@ output:
     toc_float: true
     toc_depth: 2
 vignette: >
-  %\VignetteIndexEntry{Exploring and Analyzing LC-MS Data with *Spectra* and *xcms*}
+  %\VignetteIndexEntry{Exploring and Analyzing LC-MS Data with Spectra and xcms}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding[utf8]{inputenc}
   %\VignettePackage{xcmsTutorials}

From 8f57740b9a2a082f9a5235d340f5e6c77b04468d Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Tue, 26 Sep 2023 13:28:35 +0200
Subject: [PATCH 10/42] Bump version

---
 .github/workflows/build_deploy.yaml | 2 +-
 DESCRIPTION                         | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/build_deploy.yaml b/.github/workflows/build_deploy.yaml
index 8161cab..f5aa6eb 100644
--- a/.github/workflows/build_deploy.yaml
+++ b/.github/workflows/build_deploy.yaml
@@ -17,7 +17,7 @@ jobs:
       - name: Query dependencies
         run: |
           install.packages('remotes')
-          # saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2)
+          saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2)
         shell: Rscript {0}
 
       - name: Install rcmdcheck
diff --git a/DESCRIPTION b/DESCRIPTION
index 8d883a7..5d3604e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: xcmsTutorials
 Title: Exploring and Analyzing LC-MS data with Spectra and xcms
-Version: 0.2.0
+Version: 0.99.0
 Authors@R: c(
 	     person(given = "Johannes", family = "Rainer",
 		    email = "Johannes.Rainer@eurac.edu",

From 7735bfa95b5a2024335fe3d321d0ded185387d06 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Tue, 26 Sep 2023 14:23:51 +0200
Subject: [PATCH 11/42] ci: remove installation of dependencies

---
 .github/workflows/build_deploy.yaml | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/.github/workflows/build_deploy.yaml b/.github/workflows/build_deploy.yaml
index f5aa6eb..7212c08 100644
--- a/.github/workflows/build_deploy.yaml
+++ b/.github/workflows/build_deploy.yaml
@@ -14,12 +14,6 @@ jobs:
       ## If they update their steps, we will also need to update ours.
       - uses: actions/checkout@v2
 
-      - name: Query dependencies
-        run: |
-          install.packages('remotes')
-          saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2)
-        shell: Rscript {0}
-
       - name: Install rcmdcheck
         run: |
           install.packages('rcmdcheck')

From e8e39a1442201c8ec94fc70e18c9cd88925aa5ee Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Tue, 26 Sep 2023 15:00:08 +0200
Subject: [PATCH 12/42] docs: bump version

---
 DESCRIPTION | 6 +++---
 NEWS.md     | 7 +++++++
 2 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 5d3604e..1468948 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: xcmsTutorials
 Title: Exploring and Analyzing LC-MS data with Spectra and xcms
-Version: 0.99.0
+Version: 0.99.1
 Authors@R: c(
 	     person(given = "Johannes", family = "Rainer",
 		    email = "Johannes.Rainer@eurac.edu",
@@ -21,9 +21,9 @@ Encoding: UTF-8
 LazyData: false
 Roxygen: list(markdown = TRUE)
 Depends:
-    R (>= 4.1),
+    R (>= 4.3),
     xcms (>= 3.99.0),
-    Spectra (>= 1.5.8),
+    Spectra (>= 1.11.10),
     MsExperiment
 Suggests:
     MetaboCoreUtils,
diff --git a/NEWS.md b/NEWS.md
index a384080..8bba677 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,10 @@
+# xcmsTutorials 0.99
+
+## Changes in xcmsTutorials 0.99.1
+
+- Add requirements for `xcms` and `Spectra` packages as well as R version.
+
+
 # xcmsTutorials 0.2
 
 ## Changes in xcmsTutorials 0.2.0

From 56db25e54df7ccaaabe239cfe2e99d8b7747299f Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Wed, 27 Sep 2023 07:54:14 +0200
Subject: [PATCH 13/42] Add additional visualizations

---
 vignettes/xcms-preprocessing.Rmd | 126 +++++++++++++++++++++----------
 1 file changed, 88 insertions(+), 38 deletions(-)

diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index 2dab64f..1f2a52a 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -23,6 +23,8 @@ bibliography: references.bib
 library("BiocStyle")
 library("knitr")
 library("rmarkdown")
+library("xcms")
+register(SerialParam())
 set.seed(123)
 opts_chunk$set(message = FALSE, error = FALSE, warning = FALSE,
                cache = FALSE, fig.width = 10, fig.height = 7)
@@ -1724,72 +1726,120 @@ also to evaluate the performance (and quality) of a preprocessing. LC-MS data is
 particularly difficult to visualize because its 3-dimensional nature. Focusing
 on specific *m/z* retention time ranges (EICs) can help. In this section we
 provide some additional examples how such data can be visualized. In particular,
-we show how multiple EICs can be combined in a single plot.
+we show an alternative way to evaluate and investigate identified
+chromatographic peaks in a data file.
 
-```{r}
+We below subset the data to the first sample and visualize the identified
+chromatographic peaks in the *m/z* - retention time plane.
+
+```{r, fig.cap = "Position of identified chromatographic peaks in the first sample."}
+#' Plot identified chromatographic peaks in the first sample
 data_1 <- data[1]
 plotChromPeaks(data)
 
 ```
 
-- same data, just "3 dimensional"
+While this plot provides some information on the region in which peaks were
+identified and on the retention time widths of the peaks, it does not allow to
+investigate peak shapes or intensities. Such information would be provided, for
+a single ion, by an extracted ion chromatogram. We thus below extract ion
+chromatograms for every identified chromatographic peak in the first
+sample. With parameter `expandRt = 4` we extract also signal measured 2 seconds
+before and after the peak.
 
 ```{r}
-#' Full data for rt range
-cpks <- chromPeaks(data_1, rt = c(20, 220), type = "any")
+chrs_all <- chromPeakChromatograms(data_1, expandRt = 4)
+```
 
-chrs_1 <- chromatogram(data_1, mz = cpks[, c("mzmin", "mzmax")],
-                       rt = cbind(rep(20, nrow(cpks)),
-                                  rep(220, nrow(cpks))))
+We could now plot each of the `r length(chrs_all)` EICs separately (using the
+`plot` function). As an alternative, we can use the `plotChromatogramsOverlay`
+function from the *xcms* package to plot multiple EICs into the same plot. By
+setting parameter `stacked` to a value different then `0` it is possible to
+*stack* the chromatograms along the y-axis hence providing a 3-dimensional
+impression of the data. For easier visualization we in addition increase the
+transparency of the colors for the individual lines (parameter `col`), and the
+identified chromatographic peaks (parameter `peakCol` and `peakBg` for the
+foreground and background color, respectively).
 
-y <- plotChromatogramsOverlay(chrs_1, stacked = 1, bty = "n",
-                              col = "#00000020", peakCol = "#00000020",
-                              peakBg = "#00000020")
 
-#' focus on an area, where peaks seem "darker"
-abline(v = c(130, 150), lty = 3, col = "#ff000080")
-chrs_1s <- filterRt(chrs_1, rt = c(130, 150))
+```{r, fig.cap = "All identified chromatographic peaks in one sample shown as a pseudo 3-dimensional plot."}
+plotChromatogramsOverlay(chrs_all, stacked = 1, bty = "n",
+                         col = "#00000020", peakCol = "#00000020",
+                         peakBg = "#00000020")
+```
 
-y <- plotChromatogramsOverlay(chrs_1s, stacked = 1, bty = "n",
-                              col = "#00000020", peakCol = "#00000020",
-                              peakBg = "#00000020")
+Instead of the full data range we can also extract EICs from selected regions of
+the data. Below we define an area from a *m/z* value from 113 to 119 and a
+retention time from 125 to 145 seconds.
 
-#' Focus on an area with "darker" peaks.
+```{r}
 plotChromPeaks(data_1)
-rect(125, 113, 145, 119, lty = 3, border = "#ff000080")
+rect(125, 113, 145, 119, lty = 3, border = "#ff000080", lwd = 2)
+```
+
+We next extract chromatographic peaks from that region, define *m/z* and
+retention time ranges from that and extract ion chromatograms for these slices.
+
+```{r}
+#' Extract chrom peaks from that region
+pks <- chromPeaks(data_1, mz = c(113, 119), rt = c(125, 145))
+
+#' Extract EICs for the m/z slices of the chromatographic peaks
+#' and the full retention time window of the area
+chrs_sub <- chromatogram(data_1, mz = pks[, c("mzmin", "mzmax")],
+                         rt = cbind(rep(125, nrow(pks)),
+                                    rep(145, nrow(pks))))
 
-cpks <- chromPeaks(data_1, rt = c(125, 145), mz = c(113, 119))
-chrs_1s <- chromatogram(data_1, mz = cpks[, c("mzmin", "mzmax")],
-                        rt = cbind(rep(125, nrow(cpks)),
-                                   rep(145, nrow(cpks))))
-y <- plotChromatogramsOverlay(chrs_1s, stacked = 1, bty = "n",
+```
+
+We can now plot the EICs from that region with the
+`plotChromatogramsOverlay`. This function also silently returns the y-position
+of the individual EICs in the plot which we assign to a variable `y` and use
+that information to indicate the *m/z* for the EICs along the y-axis.
+
+```{r, fig.cap = "EICs from a selected *m/z* - retention time region."}
+#' Plot the EICs of the selected area
+y <- plotChromatogramsOverlay(chrs_sub, stacked = 1, bty = "n",
                               col = "#00000020", peakCol = "#00000020",
                               peakBg = "#00000020")
-abline(h = y[[1]], col = "grey")
-mzs <- format(rowMeans(mz(chrs_1s)), digits = 5)
+
+#' Draw horizonal lines
+abline(h = y[[1]], col = "#00000020", lty = 3)
+#' Add m/z values of the EICs to the plot
+mzs <- format(rowMeans(mz(chrs_sub)), digits = 5)
 text(x = rep(126, length(mzs)), y = y[[1]],
      labels = paste0("m/z = ", mzs), pos = 1)
 
+```
 
-#' Focus on a whole rt range
-plotChromPeaks(data_1)
-abline(v = c(185, 210), lty = 3, col = "#ff000080")
+Some of the EICs seem to represent signals from isotopes (e.g. the EIC at 114.07
+and 115.07).  The infrastructure around *xcms* thus allows to create customized
+visualizations of the data. Note however, that the plots created above only
+showed **detected** chromatographic peaks in a sample but not the *full* MS data
+(i.e. signals outside of identified chromatographic peaks).
 
-chrs_1s <- filterRt(chrs_1, rt = c(185, 210))
-y <- plotChromatogramsOverlay(chrs_1s, stacked = 1, bty = "n",
-                              col = "#00000020", peakCol = "#00000020",
-                              peakBg = "#00000020")
+```{r}
+#' What could the differences be?
+pks <- pks[order(pks[, "mz"]), ]
+library(MetaboCoreUtils)
+isotopologues(pks[, c("mz", "into")])
 
+tmp <- mass2mz(123, adductNames())
 
+diffs <- outer(tmp[1, ], tmp[1, ], "-")
+diffs[lower.tri(diffs, TRUE)] <- NA
+diffs <- abs(diffs)
 
-```
+pk_diffs <- outer(pks[, "mz"], pks[, "mz"], "-")
+pk_diffs[lower.tri(pk_diffs, TRUE)] <- NA
+pk_diffs <- abs(pk_diffs)
 
-TODO: continue here:
+res <- outer(pk_diffs, diffs, "-")
 
-- `plotChromPeaks`
-- extract chromatograms for a specific rt range and full *m/z*.
-- visualize that as a stacked plot.
+sort(unique(diffs))
+```
 
+LLLLLLLL
 
 As an alternative, is it also possible to visualize the two EICs in a combined
 plot using the `plotChromatogramsOverlay` functions. This allows an immediate

From 6f22696497bf918d393368960ffb27decf95aa98 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Wed, 27 Sep 2023 09:15:31 +0200
Subject: [PATCH 14/42] docker: install xcms from the jomain branch

---
 Dockerfile                       | 2 +-
 vignettes/xcms-preprocessing.Rmd | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/Dockerfile b/Dockerfile
index 2ac0117..2f9e7ae 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -11,6 +11,6 @@ WORKDIR /home/rstudio
 COPY --chown=rstudio:rstudio . /home/rstudio/
 
 ## Install the xcmsTutorials package and additional required packages
-RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); BiocManager::install(ask = FALSE); BiocManager::install('sneumann/xcms')"
+RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); BiocManager::install(ask = FALSE); BiocManager::install('sneumann/xcms', ref = 'jomain')"
 
 RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); devtools::install('.', dependencies = TRUE, build_vignettes = TRUE, repos = BiocManager::repositories())"
diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index 1f2a52a..75ea0b3 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -46,7 +46,8 @@ which is followed by the description of a simple data centroiding approach and
 finally we present an *xcms*-based LC-MS data preprocessing that comprises
 chromatographic peak detection, alignment and correspondence. Data normalization
 procedures, compound identification and differential abundance analysis are not
-covered here. Particular emphasis is given on deriving and defining data set-dependent values for the most critical *xcms* preprocessing parameters.
+covered here. Particular emphasis is given on deriving and defining data
+set-dependent values for the most critical *xcms* preprocessing parameters.
 
 
 # Introduction

From dd62661dbbb6224e8f182195dc9304f35bffa387 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Wed, 27 Sep 2023 09:28:24 +0200
Subject: [PATCH 15/42] Add additional visualizations and update requirements
 for xcms

---
 DESCRIPTION | 6 +++---
 NEWS.md     | 4 ++++
 2 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 1468948..1210205 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: xcmsTutorials
 Title: Exploring and Analyzing LC-MS data with Spectra and xcms
-Version: 0.99.1
+Version: 0.99.2
 Authors@R: c(
 	     person(given = "Johannes", family = "Rainer",
 		    email = "Johannes.Rainer@eurac.edu",
@@ -22,7 +22,7 @@ LazyData: false
 Roxygen: list(markdown = TRUE)
 Depends:
     R (>= 4.3),
-    xcms (>= 3.99.0),
+    xcms (>= 3.99.4),
     Spectra (>= 1.11.10),
     MsExperiment
 Suggests:
@@ -36,7 +36,7 @@ Suggests:
     RColorBrewer,
     SummarizedExperiment
 Remotes:
-    sneumann/xcms
+    sneumann/xcms@jomain
 URL: https://jorainer.github.io/xcmsTutorials/
 BugReports: https://github.com/jorainer/xcmsTutorials/issues/new
 VignetteBuilder: knitr
diff --git a/NEWS.md b/NEWS.md
index 8bba677..02cc006 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,9 @@
 # xcmsTutorials 0.99
 
+## Changes in xcmsTutorials 0.99.2
+
+- Add additional visualization options. Require `xcms` version >= 3.99.4.
+
 ## Changes in xcmsTutorials 0.99.1
 
 - Add requirements for `xcms` and `Spectra` packages as well as R version.

From 0643cd7153233377914b5a01e844620340f2e192 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Fri, 29 Sep 2023 11:03:35 +0200
Subject: [PATCH 16/42] Finalize the additional visualization paragraph

---
 DESCRIPTION                      |   2 +-
 NEWS.md                          |   4 +
 README.md                        |  13 +-
 vignettes/xcms-preprocessing.Rmd | 330 +++++++++++++++----------------
 4 files changed, 173 insertions(+), 176 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 1210205..7080175 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: xcmsTutorials
 Title: Exploring and Analyzing LC-MS data with Spectra and xcms
-Version: 0.99.2
+Version: 0.99.3
 Authors@R: c(
 	     person(given = "Johannes", family = "Rainer",
 		    email = "Johannes.Rainer@eurac.edu",
diff --git a/NEWS.md b/NEWS.md
index 02cc006..3724c7b 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,9 @@
 # xcmsTutorials 0.99
 
+## Changes in xcmsTutorials 0.99.3
+
+- Finalize the additional visualization paragraph.
+
 ## Changes in xcmsTutorials 0.99.2
 
 - Add additional visualization options. Require `xcms` version >= 3.99.4.
diff --git a/README.md b/README.md
index 0dc86bb..b2b6c76 100644
--- a/README.md
+++ b/README.md
@@ -14,9 +14,7 @@ work with mass spectrometry
 [Spectra](https://github.com/RforMassSpectrometry/Spectra)) and specifically
 LC-MS data ([xcms](https://github.com/sneumann/xcms)) and walks through the
 preprocessing of a small data set emphasizing on selection of data-dependent
-settings for the individual pre-processing steps. The present workshop
-represents an updated version of the workshop given at the Metabolomics Society
-conference 2018 in Seattle (http://metabolomics2018.org).
+settings for the individual pre-processing steps.
 
 Covered topics are:
 - Data import and representation.
@@ -29,10 +27,11 @@ Covered topics are:
 - Correspondence (grouping of chromatographic peaks across samples).
 
 The full R code of all examples along with comprehensive descriptions is
-provided in the [xcms-preprocessing.Rmd](./xcms-preprocessing.Rmd) file. This
-file can be opened with e.g. RStudio which allows execution of the individual R
-commands (see section below for additionally required R packages). The R command
-`rmarkdown::render("xcms-preprocessing.Rmd")` would generate the html file
+provided in the [xcms-preprocessing.Rmd](./vignettes/xcms-preprocessing.Rmd)
+file. This file can be opened with e.g. RStudio which allows execution of the
+individual R commands (see section below for additionally required R
+packages). The R command `rmarkdown::render("xcms-preprocessing.Rmd")` would
+generate the html file
 [xcms-preprocessing.html](https://jorainer.github.io/xcmsTutorials/xcms-preprocessing.html).
 
 
diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index 75ea0b3..19a2fcf 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -116,6 +116,15 @@ Naming conventions and terms used in this document are:
   samples/files.
 
 
+## Goals of this workshop
+
+- Learn how R/*xcms* and the packages from the RforMassSpectrometry initiative
+  can be used to inspect, evaluate and analyze LC-MS data.
+
+- Learn the basis to build reproducible analysis workflows, tailored and
+  customized for individual data sets.
+
+
 # Data import and exploration
 
 The example data set of this workflow consists of two files in mzML format with
@@ -829,6 +838,7 @@ approaches).
 ```{r centWave-adapted, fig.cap = "EIC for Serine with detected chromatographic peak", results = "hide"}
 cwp <- CentWaveParam(peakwidth = c(2, 10), integrate = 2)
 
+#' Run peak detection on the EIC
 serine_chr <- findChromPeaks(serine_chr, param = cwp)
 
 #' Plot the data and higlight identified peak area
@@ -848,6 +858,7 @@ function to extract the information on identified chromatographic peaks from our
 object.
 
 ```{r chromPeaks-chromatogram}
+#' Extract identified chromatographic peaks from the EIC
 chromPeaks(serine_chr)
 ```
 
@@ -917,6 +928,7 @@ We can also express these differences in ppm (parts per million) of the average
 *m/z* of the peaks.
 
 ```{r}
+#' Differences in m/z values expressed as ppm
 mz_diff * 1e6 / mean(unlist(mz(srn_1)))
 ```
 
@@ -928,7 +940,7 @@ tested ions. We can next perform the peak detection on the full data set using
 our settings for the `ppm` and `peakwidth` parameters.
 
 ```{r findPeaks-centWave}
-#' Perform peak detection
+#' Perform peak detection on the full data set
 cwp <- CentWaveParam(peakwidth = c(2, 10), ppm = 30, integrate = 2)
 data <- findChromPeaks(data, param = cwp)
 ```
@@ -992,21 +1004,18 @@ plotChromPeaks(data, 2)
 Again, similar pattern are expected to be present for the two data files.
 
 After chromatographic peak detection it is generally a good idea to visually
-inspect individual chromatographic peaks to evaluate the performance of the peak
-detection step. This could be done by plotting EICs of known compounds/ions in
-the data or of randomly selected chromatographic peaks. To illustrate this we
-define below a matrix with *m/z* and retention time ranges for a set of
-(pre)selected peaks. The same matrix could also be created either by randomly
-selecting peaks or by defining it manually. For our example we further expand
-the retention time and *m/z* ranges of the peaks, extract the EICs with the
-`chromatogram` function and finally plot these. Identified chromatographic peaks
-within the plotted regions will by default be highlighted in a semitransparent
-grey color.
+inspect individual chromatographic peaks and evaluate the performance of the
+peak detection step. This could be done by plotting EICs of known compounds/ions
+in the data or by randomly select chromatographic peaks. *m/z* - retention time
+regions for random peaks could be defined using the example code below.
 
 ```{r}
-#' Extract m/z-rt regions for selected peaks
-mz_rt <- chromPeaks(data)[c("CP172", "CP100", "CP325", "CP618"),
-                          c("rtmin", "rtmax", "mzmin", "mzmax")]
+#' Select 4 random peaks
+idx <- sample(seq_len(nrow(chromPeaks(data))), 4)
+
+#' Extract m/z-rt regions for 4 random peaks
+mz_rt <- chromPeaks(data)[idx, c("rtmin", "rtmax", "mzmin", "mzmax")]
+
 #' Expand the rt range by 10 seconds on both sides
 mz_rt[, "rtmin"] <- mz_rt[, "rtmin"] - 10
 mz_rt[, "rtmax"] <- mz_rt[, "rtmax"] + 10
@@ -1014,9 +1023,23 @@ mz_rt[, "rtmax"] <- mz_rt[, "rtmax"] + 10
 mz_rt[, "mzmin"] <- mz_rt[, "mzmin"] - 0.005
 mz_rt[, "mzmax"] <- mz_rt[, "mzmax"] + 0.005
 
+```
+
+For our example we however manually define *m/z* - retention time regions
+(similarly as it could be done for known compounds). Below we extract the EICs
+for these regions with the `chromatogram` function and subsequently plot
+them. Identified chromatographic peaks within the plotted regions will by
+default be highlighted in a semitransparent grey color.
+
+```{r}
+#' Define m/z - retention time regions for EICs
+mz_rt <- rbind(c(106.045, 106.055, 165, 195),
+               c(132.096, 132.107, 135, 160),
+               c(125.981, 125.991, 195, 215),
+               c(105.468, 105.478, 190, 215))
+
 #' Extract the EICs
-eics <- chromatogram(data, rt = mz_rt[, c("rtmin", "rtmax")],
-                     mz = mz_rt[, c("mzmin", "mzmax")])
+eics <- chromatogram(data, mz = mz_rt[, 1:2], rt = mz_rt[, 3:4], )
 #' Plot the EICs
 plot(eics)
 ```
@@ -1058,12 +1081,11 @@ the top right plot separate. We next apply this same peak refinement on the full
 data set.
 
 ```{r}
-#' Perform peak refinement
+#' Perform peak refinement on the full data set
 data <- refineChromPeaks(data, param = mpp)
 ```
 
 
-
 ## Alignment
 
 While chromatography helps to better discriminate between analytes it is also
@@ -1207,9 +1229,11 @@ chromatographic peaks in the returned chromatographic data.
 
 ```{r bpc-raw-adjusted, fig.cap = "BPC before (top) and after (bottom) alignment."}
 par(mfrow = c(2, 1))
+
 #' Plot the raw base peak chromatogram
 plot(bpc_raw)
 grid()
+
 #' Plot the BPC after alignment
 plot(chromatogram(data, aggregationFun = "max", chromPeaks = "none"))
 grid()
@@ -1222,6 +1246,8 @@ plot the EIC for the *[M+H]+* ion for serine before and after alignment.
 
 ```{r serine-xic-adjusted, fig.cap = "EIC for Serine before (left) and after (right) alignment"}
 par(mfrow = c(1, 2), mar = c(4, 4.5, 1, 0.5))
+
+#' EIC before alignment
 plot(serine_chr)
 grid()
 #' EIC after alignment
@@ -1373,7 +1399,7 @@ least 50% of samples per sample group.
 pdp <- PeakDensityParam(sampleGroups = sampleData(data)$group, bw = 1.8,
                         minFraction = 0.4, binSize = 0.02)
 
-#' Perform the correspondence analysis
+#' Perform the correspondence analysis on the full data
 data <- groupChromPeaks(data, param = pdp)
 data
 ```
@@ -1392,7 +1418,6 @@ chr_2 <- chromatogram(data, mz = 118.08625 + c(-0.01, 0.01),
                       aggregationFun = "max")
 #' Setting simulate = FALSE to show the actual correspondence results
 plotChromPeakDensity(chr_2, simulate = FALSE)
-
 ```
 
 We evaluate the results also on a different slice containing signal for ions
@@ -1473,19 +1498,20 @@ a truly missing value (because the ion/compound was not present in that sample)
 or a failure of the peak detection algorithm to identify a peak (either because
 the measured signal was too noisy, or too low, or a combination of both).
 
-To illustrate this we below extract the *m/z* - retention time region for
-selected features, extract their EICs and plot them (using a different color for
-each sample).
+To illustrate this we below define *m/z* - retention time regions containing
+signal of features with missing values, extract their EICs and plot them (using
+a different color for each sample). Note: these regions were identified by first
+visually inspecting EICs for all features with at least one missing value.
 
 ```{r features-missing-values, fig.cap = "Examples of features for which a peak was only identified in one sample."}
-#' Extract the m/z-rt region for selected peaks
-mz_rt <- featureArea(data, features = c("FT026", "FT028", "FT246", "FT306"))
-mz_rt[, "rtmin"] <- mz_rt[, "rtmin"] - 2
-mz_rt[, "rtmax"] <- mz_rt[, "rtmax"] + 2
+#' Define m/z - rt regions for selected examples with missing peaks
+mz_rt <- rbind(c(109.661, 109.664, 192, 200),
+               c(109.993, 109.998, 200, 215),
+               c(125.586, 125.591, 195, 215),
+               c(130.959, 130.961, 197, 201))
 
 #' Extract their EICs and plot them
-chrs <- chromatogram(data, mz = mz_rt[, c("mzmin", "mzmax")],
-                     rt = mz_rt[, c("rtmin", "rtmax")])
+chrs <- chromatogram(data, mz = mz_rt[, 1:2], rt = mz_rt[, 3:4])
 plot(chrs, col = c("red", "blue"), lwd = 2)
 ```
 
@@ -1493,16 +1519,16 @@ Indeed, for all these feature, chromatographic peak detection failed to identify
 a peak in one of the two samples. For the features in the upper panel, the
 signal was most likely too low, while for the bottom left feature the signal was
 likely too noisy, and for the bottom right too sparse (i.e. to few data points
-to properly detect a peak). In all cases, however, signal from (presumably)
-the same ion was measured in both samples. Thus, reporting a missing value would
-not be correct for these cases.
+to properly detect a peak). In all cases, however, signal from (presumably) the
+same ion was measured in both samples. Thus, reporting a missing value would not
+be correct for these.
 
 The aim of the *gap filling* is now to rescue signal for such features by
 integrating the intensities measured within the feature's *m/z* - retention time
 area in the sample(s) in which no chromatographic peak was detected. In *xcms*
 this can be done with the `fillChromPeaks` function and the `ChromPeakAreaParam`
-which allows to configure this algorithm. Below we perform this gap filling
-summing also the number of missing values before and after running
+which allows to configure this algorithm. Below we perform the gap filling
+showing also the number of missing values before and after running
 `fillChromPeaks`.
 
 ```{r fillChromPeaks}
@@ -1517,13 +1543,24 @@ sum(is.na(featureValues(data)))
 ```
 
 With `fillChromPeaks` we could thus *rescue* signal for all but 26
-features. Below we extract the EICs for these and visually inspect some of them.
+features. Also for the 4 example features from above a signal was
+filled-in. Below we visualize the gap-filled chromatographic peaks for these.
 
-```{r gap-filling-missing, fig.cap = "Features with missing values even after gap-filling."}
+```{r features-missing-values-filled, fig.cap = "Features with filled-in signal."}
+#' Extract their EICs and plot them
+chrs <- chromatogram(data, mz = mz_rt[, 1:2], rt = mz_rt[, 3:4])
+plot(chrs, col = c("red", "blue"), lwd = 2)
+```
+
+In contrast, we identify and plot the EICs for features for which no signal
+could be filled-in (i.e. which still have missing values in one of the two
+samples).
+
+```{r gap-filling-missing, fig.cap = "Features with missing values even after gap-filling.", fig.width = 12, fig.height = 10}
 #' Identify features with missing values
 fts <- rownames(featureValues(data))[is.na(rowSums(featureValues(data)))]
 
-#' Define m/z - rt regions for these features
+#' Extract the m/z - rt regions for these features
 mz_rt <- featureArea(data, features = fts)
 
 #' Expand the retention time by 1 second on both sides
@@ -1536,42 +1573,28 @@ chrs_na <- chromatogram(data, mz = mz_rt[, c("mzmin", "mzmax")],
 plot(chrs_na, col = c("red", "blue"), lwd = 2)
 ```
 
-For these features indeed no signal was measured in the second sample and the
-signal for most of them is also very low in the sample in which a
-chromatographic peak was detected.
-
-In contrast, for the 4 example features from above a signal was *rescued*. Below
-we visualize the gap-filled chromatographic peaks for these.
-
-```{r features-missing-values-filled, fig.cap = "Features with filled-in signal."}
-#' Extract the m/z-rt region for selected peaks
-mz_rt <- featureArea(data, features = c("FT026", "FT028", "FT246", "FT306"))
-mz_rt[, "rtmin"] <- mz_rt[, "rtmin"] - 2
-mz_rt[, "rtmax"] <- mz_rt[, "rtmax"] + 2
-
-#' Extract their EICs and plot them
-chrs <- chromatogram(data, mz = mz_rt[, c("mzmin", "mzmax")],
-                     rt = mz_rt[, c("rtmin", "rtmax")])
-plot(chrs, col = c("red", "blue"), lwd = 2)
-```
+For these features indeed no signal was measured in the second sample.
 
-A nice way to check that gap-filling was able to correctly rescue signals is to
-plot, for features with at least one missing value, the average detected against
-the average filled-in signal. Ideally, this should be done on QC samples or
-other repeatedly measured samples were no difference in feature abundance
-between samples is expected.
+An alternative way to confirm if gap-filling was able to correctly rescue
+signals is to plot, for features with at least one missing value, the average
+detected against the average filled-in signal. Ideally, this should be done on
+QC samples or other repeatedly measured samples were no difference in feature
+abundances between samples is expected.
 
 The code below extracts first only the detected feature values (by setting
 `filled = FALSE` in the `featureValues` call), then the detected **and**
-filled-in signal in which the detected signal is subsequently replaced with
-`NA`. Finally, after calculating the row averages for both matrices (excluding
-missing values), the values are plotted against each other.
+filled-in signal. For the latter, the detected signal is subsequently replaced
+with `NA` to create a data matrix with only filled-in values. Finally, after
+calculating the row averages for both matrices (excluding missing values), these
+values are plotted against each other.
 
 ```{r comparison-detected-filled, fig.cap = "Detected (x-axis) against filled (y-axis) signal. The black solid line represents the identity line."}
 #' Get only detected signal
 vals_detect <- featureValues(data, filled = FALSE)
+
 #' Get detected and filled-in signal
 vals_filled <- featureValues(data)
+
 #' Replace detected signal with NA
 vals_filled[!is.na(vals_detect)] <- NA
 
@@ -1593,6 +1616,7 @@ plot(log2(avg_detect), log2(avg_filled),
      pch = 21, bg = "#00000080")
 grid()
 abline(0, 1)
+
 ```
 
 As expected, detected signal is generally higher than filled-in signal. For the
@@ -1600,6 +1624,20 @@ biggest part (in particular for higher intensities), filled-in and detected
 feature values are similar suggesting that the gap filling step indeed rescued
 signal.
 
+We could also calculate statistics on these values. Below we fit a linear
+regression line to the data and summarize its result.
+
+```{r}
+#' fit a linear regression line to the data
+l <- lm(log2(avg_filled) ~ log2(avg_detect))
+summary(l)
+```
+
+With a value of 1.005, the slope of the line is thus very close to the slope of
+the identity line and the two sets of values are also highly correlated (R
+squared of 0.81).
+
+
 
 ## Preprocessing result
 
@@ -1710,76 +1748,88 @@ workshops/tutorials at
 
 # Final words
 
-- don't use defaults of *xcms*.
-- use the infrastructure to inspect and summarize the data.
-- don't limit yourself to just *xcms*, use the vast number of additional
-  packages, methods, visualizations in R.
-- build custom workflows, data visualization approaches or your own analysis
-  method.
+- Don't use default settings for preprocessing algorithms.
+- Use the infrastructure provided by the RforMassSpectrometry package ecosystem
+  to inspect, explore and summarize the data.
+- Unleash the power of R!
+  - Create own visualization/summarization functions if needed.
+  - Combine functionalities from different packages.
+  - Create customized (and reproducible) analysis workflows.
 
 
 # Appendix
 
+
 ## Additional visualizations
 
-Visualization is key to understand the signal measured by the MS instrument and
-also to evaluate the performance (and quality) of a preprocessing. LC-MS data is
-particularly difficult to visualize because its 3-dimensional nature. Focusing
-on specific *m/z* retention time ranges (EICs) can help. In this section we
-provide some additional examples how such data can be visualized. In particular,
-we show an alternative way to evaluate and investigate identified
-chromatographic peaks in a data file.
+Visualization is key to understand the signal measured by an MS instrument and
+also to evaluate the performance (and quality) of the preprocessing. However,
+LC-MS data is particularly difficult to visualize because of its 3-dimensional
+nature. Focusing on specific *m/z* - retention time ranges (EICs) can help, but
+provides only information on small subsets of the whole data. In this section we
+present an alternative approach to visualize the whole chromatographic peak
+space from an LC-MS experiment.
 
 We below subset the data to the first sample and visualize the identified
-chromatographic peaks in the *m/z* - retention time plane.
+chromatographic peaks in the *m/z* - retention time plane using the
+`plotChromPeaks` data function already used before.
 
 ```{r, fig.cap = "Position of identified chromatographic peaks in the first sample."}
 #' Plot identified chromatographic peaks in the first sample
 data_1 <- data[1]
-plotChromPeaks(data)
+plotChromPeaks(data_1)
 
 ```
 
-While this plot provides some information on the region in which peaks were
+While this plot provides information on the region in which peaks were
 identified and on the retention time widths of the peaks, it does not allow to
-investigate peak shapes or intensities. Such information would be provided, for
-a single ion, by an extracted ion chromatogram. We thus below extract ion
+investigate peak shapes or intensities. Such information would be provided (for
+a single ion) by an extracted ion chromatogram. We thus below extract ion
 chromatograms for every identified chromatographic peak in the first
-sample. With parameter `expandRt = 4` we extract also signal measured 2 seconds
-before and after the peak.
+sample. With parameter `expandRt = 4` we increase for each chromatographic peak
+the region from which the data will be extracted by 4 seconds on either side.
 
 ```{r}
+#' Extract EICs for each identified chromatographic peak
 chrs_all <- chromPeakChromatograms(data_1, expandRt = 4)
 ```
 
-We could now plot each of the `r length(chrs_all)` EICs separately (using the
-`plot` function). As an alternative, we can use the `plotChromatogramsOverlay`
-function from the *xcms* package to plot multiple EICs into the same plot. By
-setting parameter `stacked` to a value different then `0` it is possible to
-*stack* the chromatograms along the y-axis hence providing a 3-dimensional
-impression of the data. For easier visualization we in addition increase the
-transparency of the colors for the individual lines (parameter `col`), and the
-identified chromatographic peaks (parameter `peakCol` and `peakBg` for the
-foreground and background color, respectively).
-
+While we could now simply proceed and plot each of the `r length(chrs_all)` EICs
+separately, we instead use below the `plotChromatogramsOverlay` function that
+allows to plot multiple EICs into the same plot hence providing an overview of
+the full set of identified chromatographic peaks. By setting parameter `stacked`
+to a value different then `0` it is possible to *stack* the chromatograms along
+the y-axis hence providing a simple 3-dimensional impression of the data. For
+easier visualization we in addition increase the transparency of the colors for
+the individual lines (parameter `col`), and the identified chromatographic peaks
+(parameter `peakCol` and `peakBg` for the foreground and background color,
+respectively).
 
 ```{r, fig.cap = "All identified chromatographic peaks in one sample shown as a pseudo 3-dimensional plot."}
+#' Create a stacked EIC plot
 plotChromatogramsOverlay(chrs_all, stacked = 1, bty = "n",
                          col = "#00000020", peakCol = "#00000020",
                          peakBg = "#00000020")
 ```
 
-Instead of the full data range we can also extract EICs from selected regions of
-the data. Below we define an area from a *m/z* value from 113 to 119 and a
-retention time from 125 to 145 seconds.
+This plot thus provides a general overview of the detected signal
+(chromatographic peaks) of a data file. we can immediately spot some very high
+intensity peaks, regions with higher number of ions and also signal from
+potential contaminants.
 
-```{r}
+Instead of the full data range we can also zoom into a region and extract EICs
+from that for a closer inspection of the data. Below we define an area from a
+*m/z* value from 113 to 119 and a retention time from 125 to 145 seconds.
+
+```{r, fig.cap = "Selected region from which EICs should be extracted and plotted."}
+#' Plot location of all peaks and highlight region of interest
 plotChromPeaks(data_1)
 rect(125, 113, 145, 119, lty = 3, border = "#ff000080", lwd = 2)
 ```
 
-We next extract chromatographic peaks from that region, define *m/z* and
-retention time ranges from that and extract ion chromatograms for these slices.
+With the *m/z* ranges from the chromatographic peaks in that region defined, we
+can next extract ion chromatograms for the data slices defined by these *m/z*
+ranges and the selected (fixed) retention time range.
 
 ```{r}
 #' Extract chrom peaks from that region
@@ -1793,10 +1843,11 @@ chrs_sub <- chromatogram(data_1, mz = pks[, c("mzmin", "mzmax")],
 
 ```
 
-We can now plot the EICs from that region with the
-`plotChromatogramsOverlay`. This function also silently returns the y-position
-of the individual EICs in the plot which we assign to a variable `y` and use
-that information to indicate the *m/z* for the EICs along the y-axis.
+We can now plot the EICs from that region again using the
+`plotChromatogramsOverlay` function. Next to plotting the data, this function
+also silently returns the y-positions of the individual EICs in the plot. We
+assign that below to a variable `y` and use this information to draw the *m/z*
+for the EICs along the y-axis.
 
 ```{r, fig.cap = "EICs from a selected *m/z* - retention time region."}
 #' Plot the EICs of the selected area
@@ -1814,51 +1865,21 @@ text(x = rep(126, length(mzs)), y = y[[1]],
 ```
 
 Some of the EICs seem to represent signals from isotopes (e.g. the EIC at 114.07
-and 115.07).  The infrastructure around *xcms* thus allows to create customized
-visualizations of the data. Note however, that the plots created above only
-showed **detected** chromatographic peaks in a sample but not the *full* MS data
-(i.e. signals outside of identified chromatographic peaks).
+and 115.07). In fact, we can use the `isotopologues` function from the `r
+Biocpkg("MetaboCoreUtils")` to check whether pairs of *m/z* and intensity values
+would match signal expected for isotopes.
 
 ```{r}
-#' What could the differences be?
+#' Order the extracted chromatographic peaks by m/z
 pks <- pks[order(pks[, "mz"]), ]
+
+#' Test which signals could come from isotopes
 library(MetaboCoreUtils)
 isotopologues(pks[, c("mz", "into")])
-
-tmp <- mass2mz(123, adductNames())
-
-diffs <- outer(tmp[1, ], tmp[1, ], "-")
-diffs[lower.tri(diffs, TRUE)] <- NA
-diffs <- abs(diffs)
-
-pk_diffs <- outer(pks[, "mz"], pks[, "mz"], "-")
-pk_diffs[lower.tri(pk_diffs, TRUE)] <- NA
-pk_diffs <- abs(pk_diffs)
-
-res <- outer(pk_diffs, diffs, "-")
-
-sort(unique(diffs))
 ```
 
-LLLLLLLL
-
-As an alternative, is it also possible to visualize the two EICs in a combined
-plot using the `plotChromatogramsOverlay` functions. This allows an immediate
-visual comparison of the peak shapes which, for isotope peaks, are expected to
-be highly similar. In the `plotChromatogramsOverlay` function below we set
-`stacked = 0.1` which causes the EICs to be drawn slightly shifted along the
-y-axis (hence the EICs are being stacked). This gives a little 3-dimensional
-impression of the data. The `plotChromatogramsOverlay` also returns the
-y-positions of each EIC which we can then use to draw a horizontal line and
-indicate the *m/z* for each of the EICs.
-
-```{r, eval = FALSE}
-y <- plotChromatogramsOverlay(chrs, stacked = 0.1, bty = "n")
-abline(h = y[[1]], col = "grey")
-mzs <- format(rowMeans(mz(chrs)), digits = 5)
-text(x = c(175, 175), y = y[[1]],
-     labels = paste0("m/z = ", mzs), pos = 1)
-```
+Pairs of chromatographic peaks have been identified as being potential
+isotopologues.
 
 
 ## Bonus material - peak detection fun
@@ -1932,33 +1953,6 @@ Zinnen*](https://en.wikipedia.org/wiki/Tre_Cime_di_Lavaredo) (right tri-forked
 peak colored green).
 
 
-# Additional things
-
-
-Below we extract the EICs for
-two of these peaks using the `chromPeakChromatograms` function. This is a
-convenience function for easier and faster extraction of EICs for selected peaks
-(in a specific sample). The sam data (but for all samples) could also be
-extracted with the `chromatogram` function specifying the *m/z* and retention
-time range of the chromatographic peak. In the call below we provide the IDs
-(row names) of the chromatographic peaks with parameter `peaks` and expand the
-retention time range from which to extract the data by 5 seconds (on either side
-of the peak) by setting `expandRt = 5`.
-
-```{r, eval = FALSE}
-#' Extract EIC for two identified chromatographic peaks
-chrs <- chromPeakChromatograms(data, peaks = c("CP499", "CP520"), expandRt = 5)
-```
-
-We next plot these chromatograms. The identified chromatographic peaks are
-automatically highlighted, if present, with a grey color.
-
-```{r, eval = FALSE}
-plot(chrs)
-```
-
-
-
 
 # Session information
 

From 008f34a01880c38cca280d9de7e5c51d67836d0e Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Fri, 29 Sep 2023 14:43:05 +0200
Subject: [PATCH 17/42] Small fixes

---
 vignettes/xcms-preprocessing.Rmd | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index 19a2fcf..46bb91c 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -442,7 +442,7 @@ data is in profile mode.
 ```{r}
 #' Combine all spectra of one file into a single spectrum
 bps <- spectra(data) |>
-    combineSpectra(f = fromFile(data), intensityFun = max, ppm = 5)
+    combineSpectra(f = fromFile(data), ppm = 5, intensityFun = max)
 bps
 ```
 
@@ -681,8 +681,8 @@ visualize the result repeating the visualization from above.
 ```{r centroiding, fig.cap = "Centroided data for Serine."}
 #' Smooth and centroid the spectra data
 sps_cent <- spectra(data) |>
-smooth(method = "SavitzkyGolay", halfWindowSize = 6L) |>
-pickPeaks(halfWindowSize = 2L)
+    smooth(method = "SavitzkyGolay", halfWindowSize = 6L) |>
+    pickPeaks(halfWindowSize = 2L)
 
 #' replace spectra
 spectra(data) <- sps_cent

From 2bd0886cc30ba8ffb07c36bee3cb19c4c8aedee6 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Mon, 2 Oct 2023 16:48:48 +0200
Subject: [PATCH 18/42] Small updates and fixes in the data access section

---
 DESCRIPTION                      |   2 +-
 NEWS.md                          |   6 ++
 vignettes/xcms-preprocessing.Rmd | 158 ++++++++++++++++---------------
 3 files changed, 89 insertions(+), 77 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 7080175..b33e7fc 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: xcmsTutorials
 Title: Exploring and Analyzing LC-MS data with Spectra and xcms
-Version: 0.99.3
+Version: 1.0.0
 Authors@R: c(
 	     person(given = "Johannes", family = "Rainer",
 		    email = "Johannes.Rainer@eurac.edu",
diff --git a/NEWS.md b/NEWS.md
index 3724c7b..2177f4c 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,9 @@
+# xcmsTutorials 1.0
+
+## Changes in 1.0.0
+
+- Restructure and clarify basic data access section.
+
 # xcmsTutorials 0.99
 
 ## Changes in xcmsTutorials 0.99.3
diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index 46bb91c..9fafac7 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -163,6 +163,7 @@ pd <- data.frame(file = basename(fls),
                  sample = c("POOL_1", "POOL_2"),
                  injection_index = c(1, 19),
                  group = "POOL")
+
 #' Import the data of the experiment
 data <- readMsExperiment(fls, sampleData = pd)
 data
@@ -191,7 +192,7 @@ data_2 <- data[2]
 data_2
 ```
 
-This thus subsetted the full data, including sample information and spectra data
+This did subset the full data, including sample information and spectra data
 to those of the second file. Phenotype information can be retrieved with the
 `sampleData` function from an `MsExperiment` object.
 
@@ -213,26 +214,47 @@ The new version of *xcms* uses thus the more modern and flexible infrastructure
 for MS data analysis provided by the `r Biocpkg("Spectra")` package. While it is
 still possible and supported to use *xcms* together with the `r
 Biocpkg("MSnbase")` package, users are advised to switch to this new
-infrastructure as it provides more flexibility and a higher performance.
-
-This `Spectra` object represents the full LC-MS data of the experiment. Each
-element in this object is a spectrum (in one sample/file) with all information
-provided by the respective original data (mzML) file. Note that, through
-additional packages such as the `r Biocpkg("MsBackendRawFileReader")`, it would
-also be possible to import MS data from other files than mzML, mzXML or CDF
-files.
+infrastructure as it provides more flexibility and a higher performance. Also,
+through additional packages such as the `r Biocpkg("MsBackendRawFileReader")`,
+the new infrastructure would allow to import MS data from other files than mzML,
+mzXML or CDF files.
 
 In the next few examples we briefly explain the `Spectra` object and illustrate
 the use of such objects using some simple examples. More information on
 `Spectra` objects can be found in the package's
 [documentation](https://RforMassSpectrometry.github.io/Spectra) or the
-[SpectraTutorials](https://jorainer.github.io/SpectraTutorials). Besides the
-peak data (*m/z* and intensity values) also additional spectra variables
-(metadata) are available in a `Spectra` object. These can be listed using the
-`spectraVariables` function that we call on our example MS data below. To avoid
-nested function calls and hence improve the readability of the code, we use the
-R pipe operator `|>` that allows to concatenate consecutive calls in a more
-readable fashion.
+[SpectraTutorials](https://jorainer.github.io/SpectraTutorials).
+
+The `Spectra` object contains the full MS data of the experiment. It's length is
+thus equal to the total number of spectra within the experiment. Below we
+determine this number for our example data set. To avoid nested function calls
+and hence improve the readability of the code, we use the R pipe operator `|>`
+that allows to concatenate consecutive calls in a more readable fashion.
+
+```{r}
+#' Get the total number of spectra
+spectra(data) |>
+    length()
+```
+
+The `Spectra` object itself is agnostic of any sample information, it simply
+contains all spectra from the experiment, first all spectra from the first file,
+followed by the spectra from the second. The mapping of spectra to samples is
+provided by the `MsExperiment` object. To access spectra from a specific sample
+we either subset the `MsExperiment` to that particular sample (as done in the
+example above) or we use the `fromFile` function that returns for each spectrum
+the index of the file within the `MsExperiment` to which it belongs. Below we
+use that function to determine the total number of spectra for each sample.
+
+```{r}
+#' Get the number of spectra per file.
+fromFile(data) |>
+    table()
+```
+
+Besides the peak data (*m/z* and intensity values) also additional spectra
+variables (metadata) are available in a `Spectra` object. These can be listed
+using the `spectraVariables` function that we call on our example MS data below.
 
 ```{r}
 #' List available spectra variables
@@ -256,39 +278,10 @@ spectra(data) |>
     table()
 ```
 
-The present data set contains thus 1,862 spectra, all from MS level 1. In
-`Spectra` objects, MS spectra are organized in a linear fashion, i.e. as a
-single long list of spectra. The `Spectra` thus contains spectra from both data
-files, first all spectra from the first file and then from the second, all in
-the order in which they are provided in the original data files (hence generally
-ordered by retention time). To access spectra from a specific sample we either
-subset the `MsExperiment` to that particular sample or we use the `fromFile`
-function that returns for each spectrum the index of the file within the
-`MsExperiment` to which it belongs. Below we subset our object to the first data
-file, extract the retention times of the spectra from that file and, by using
-the `head` function, display the first 6 of them.
-
-```{r}
-#' Display the first 6 retention times of spectra from the first sample
-data[1] |>
-    spectra() |>
-    rtime() |>
-    head()
-```
-
-Such basic data summaries can be helpful for a first initial quality assessment
-to potentially identify problematic data files with e.g. a unexpected low number
-of spectra or sparse spectra (i.e. spectra with very few mass peaks). As an
-example we list below the number of spectra per file in the present data set
-using `fromFile` that, as described above, returns for each spectrum in the data
-set the index of the sample/file to which it belongs to.
-
-```{r}
-#' Count the number of spectra per file
-data |>
-    fromFile() |>
-    table()
-```
+The present data set contains thus 1,862 spectra, all from MS level 1. Such
+basic data summaries can be helpful for a first initial quality assessment to
+potentially identify problematic data files with e.g. a unexpected low number of
+spectra.
 
 We could also check the number of peaks per spectrum in the different data
 files. The number of peaks per spectrum can be extracted with the `lengths`
@@ -325,17 +318,13 @@ respective values:
 ```{r}
 #' Extract m/z values
 mz(sp)
+
 #' Extract intensity values
 intensity(sp)
 ```
 
-Alternatively, the `peaksData` function could be used to extract the *m/z* and
-intensity values of a spectrum as a (two column) numeric matrix.
-
-```{r}
-#' Extract the full peaks data
-peaksData(sp)[[1]] |> head()
-```
+As an alternative, the `peaksData` function could be used to extract both the
+*m/z* and intensity (as numeric matrices) with a single function call.
 
 The total ion signal of a spectrum could be calculated by simply summing the
 intensities of all peaks in the spectrum. Below we perform that operation on the
@@ -369,17 +358,18 @@ spectra(data[2]) |>
     quantile()
 ```
 
-The total ion signals of the two data files is (as expected) similar.  Through
+The total ion signals of the two data files is (as expected) similar. Through
 the `Spectra` object we have thus the possibility to inspect and explore the
 (raw) MS data of an experiment and use its functionality to create own quality
 assessment functions. Alternatively, also the `r Biocpkg("MsQuality")` package
 could be to calculate core MS quality metrics on a full experiment
-(`MsExperiment`) or individual data files (`Spectra`). TODO: ADD REFERENCE TO
-MSQUALITY PAPER.
+(`MsExperiment`) or individual data files (`Spectra`).
 
 
 ## Data visualization
 
+### General data overview
+
 Visualization is crucial for quality assessment of MS data. For LC-MS data
 visualizing a base peak chromatogram (BPC) or total ion chromatogram (TIC) would
 for example allow to evaluate the performance of the liquid chromatography of
@@ -420,6 +410,7 @@ error/resolution of the instrument.
 Below we plot the spectra for 2 consecutive scans.
 
 ```{r, fig.cap = "Spectra from two consecutive scan of the first file"}
+#' Plot two consecutive spectra
 plotSpectra(spectra(data)[123:124], xlim = c(105, 130))
 ```
 
@@ -458,6 +449,9 @@ These BPS thus show the most common ions present in each of the two
 samples. Apparently there is quite some overlap in ion content between the two
 files.
 
+
+### Detailed data inspection
+
 Apart from such general data overview it is also possible (and also suggested)
 to explore the data in more detail. To this end we next focus on a specific
 subset of the data were we expect signal for a compound that should be present
@@ -469,6 +463,7 @@ the spectra data using the `filterRt` function extracting only spectra measured
 between 180 and 181 seconds.
 
 ```{r}
+#' Extract all spectra measured between 180 and 181 seconds
 sps <- spectra(data) |>
     filterRt(c(180, 181))
 sps
@@ -492,9 +487,9 @@ functions are at present available for `MsExperiment` objects while more are
 available for `Spectra` objects.
 
 ```{r}
-#' subset the whole MsExperiment
+#' Subset the whole MsExperiment
 data_sub <- filterRt(data, rt = c(180, 181))
-#' extract spectra from the subset for the first sample
+#' Extract spectra from the subset for the first sample
 spectra(data_sub[1])
 ```
 
@@ -502,17 +497,19 @@ For the present purpose it is however not important to keep the sample
 association intact and we thus proceed to plot the previously extracted spectra.
 
 ```{r, fig.cap = "MS1 spectra measured between 180 and 181 seconds"}
+#' Plot the spectra
 plotSpectra(sps)
 ```
 
 We can immediately spot several mass peaks in the spectrum, with the largest one
-at an *m/z* of about 130 seconds and the second largest at about 106, which
+at an *m/z* of about 130 and the second largest at about 106, which
 could represent signal for an ion of
 [Serine](https://en.wikipedia.org/wiki/Serine). Below we calculate the exact
 (monoisotopic) mass for serine from its chemical formula *C3H7NO3* using the
 `calculateMass` function from the `r Biocpkg("MetaboCoreUtils")` package.
 
 ```{r}
+#' Calculate the (monoisotopic) mass of serine
 library(MetaboCoreUtils)
 mass_serine <- calculateMass("C3H7NO3")
 mass_serine
@@ -532,6 +529,7 @@ positively charged and `adductNames("negative")` for all negatively charge
 ions).
 
 ```{r}
+#' Calculate the m/z for the [M+H]+ ion of serine
 serine_mz <- mass2mz(mass_serine, "[M+H]+")
 serine_mz
 ```
@@ -552,7 +550,7 @@ function. Note that it would also be possible to first filter the data set by
 *m/z* using the `filterMzRange` function and then extract the chromatogram.
 
 ```{r, fig.cap = "Ion trace for an ion of serine"}
-#' Extract an full RT chromatogram for ions with an m/z similar than serine
+#' Extract a full RT chromatogram for ions with an m/z similar than serine
 serine_chr <- chromatogram(data, mz = serine_mz + c(-0.05, 0.05))
 plot(serine_chr)
 ```
@@ -575,11 +573,11 @@ functions (similar to the `mz` and `intensity` functions for a `Spectra`
 object).
 
 ```{r chromatogram}
-#' get intensity valuesfor the chromatogram of the first sample
+#' Get intensity values for the chromatogram of the first sample
 intensity(serine_chr[1, 1]) |>
     head()
 
-#' get the respective retention times of the first sample
+#' Get the respective retention times of the first sample
 rtime(serine_chr[1, 1]) |>
     head()
 ```
@@ -600,9 +598,9 @@ extracted ion chromatogram (EIC, sometimes also referred to as XIC) for the
 ```{r, fig.cap = "Extracted ion chromatogram for serine."}
 #' Create an EIC for serine
 data |>
-filterRt(rt = c(175, 189)) |>
-filterMz(mz = serine_mz + c(-0.05, 0.05)) |>
-chromatogram() |>
+    filterRt(rt = c(175, 189)) |>
+    filterMz(mz = serine_mz + c(-0.05, 0.05)) |>
+    chromatogram() |>
 plot()
 ```
 
@@ -654,10 +652,11 @@ containing signal for serine and visualize the full MS data measured for that
 region in both data files.
 
 ```{r serine-profile-mode-data, fig.cap = "Profile data for Serine."}
+#' Visualize the full MS data for a small m/z - rt area
 data |>
-filterRt(rt = c(175, 189)) |>
-filterMz(mz = c(106.02, 106.07)) |>
-plot()
+    filterRt(rt = c(175, 189)) |>
+    filterMz(mz = c(106.02, 106.07)) |>
+    plot()
 ```
 
 The lower panel of the plot shows all mass peaks measured by the instrument:
@@ -684,7 +683,7 @@ sps_cent <- spectra(data) |>
     smooth(method = "SavitzkyGolay", halfWindowSize = 6L) |>
     pickPeaks(halfWindowSize = 2L)
 
-#' replace spectra
+#' Replace spectra
 spectra(data) <- sps_cent
 
 #' Plot the centroided data for Serine
@@ -880,10 +879,10 @@ consecutive scans are considered. To illustrate this, we plot again the full
 MS data for the data subset containing signal for serine.
 
 ```{r Serine-mz-scattering-plot}
-#' Restrict the data to signal from Serine
+#' Restrict to data containing signal from serine
 srn <- data |>
-filterRt(rt = c(179, 186)) |>
-filterMz(mz = c(106.04, 106.07))
+    filterRt(rt = c(179, 186)) |>
+    filterMz(mz = c(106.04, 106.07))
 
 #' Plot the data
 plot(srn)
@@ -1107,6 +1106,7 @@ run, slight drifts of the signal are visible. These were also already visible in
 the EIC for serine, that we plot again below.
 
 ```{r}
+#' Plot EIC of serine
 plot(serine_chr, xlim = c(175, 190))
 grid()
 ```
@@ -1474,7 +1474,6 @@ our example, neighboring and overlapping peaks per sample were merged to avoid
 an overestimation of the feature abundance. Below we extract the feature
 abundances and show the first 6 rows.
 
-
 ```{r}
 #' Get abundances for the first 6 features
 featureValues(data, method = "sum") |> head()
@@ -1704,6 +1703,13 @@ simply use the `assay` function.
 assay(res) |> head()
 ```
 
+The `SummarizedExperiment` can be subset by rows and/or columns.
+
+```{r}
+#' Subset to the first 10 features
+res[1:10, ]
+```
+
 
 ## What next?
 

From 73afd51a080e3884e51fd925523fef23e27b65f0 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Thu, 5 Oct 2023 11:38:00 +0200
Subject: [PATCH 19/42] Small updates and fixes.

---
 README.md                        | 11 +++++-
 vignettes/xcms-preprocessing.Rmd | 58 +++++++++++++++++---------------
 2 files changed, 41 insertions(+), 28 deletions(-)

diff --git a/README.md b/README.md
index b2b6c76..8a6bc81 100644
--- a/README.md
+++ b/README.md
@@ -17,13 +17,21 @@ preprocessing of a small data set emphasizing on selection of data-dependent
 settings for the individual pre-processing steps.
 
 Covered topics are:
+
 - Data import and representation.
+
 - Accessing, subsetting and visualizing data.
+
 - Centroiding of profile mode MS data.
+
 - Chromatographic peak detection.
+
 - Empirically determine appropriate settings for the analyzed data set.
+
 - Evaluation of identified peaks.
+
 - Alignment (retention time correction).
+
 - Correspondence (grouping of chromatographic peaks across samples).
 
 The full R code of all examples along with comprehensive descriptions is
@@ -44,12 +52,13 @@ pre-installed:
 - Get the [docker image](https://hub.docker.com/r/jorainer/xcms_tutorials) of
   this tutorial with `docker pull jorainer/xcms_tutorials:latest`.
 - Start docker using
-  ```
+```
   docker run \
       -e PASSWORD=bioc \
       -p 8787:8787 \
       jorainer/xcms_tutorials:latest
   ```
+
 - Enter `http://localhost:8787` in a web browser and log in with username
   `rstudio` and password `bioc`.
 - In the RStudio server version: open any of the R-markdown (*.Rmd*) files in
diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index 9fafac7..514c9d3 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -1,5 +1,5 @@
 ---
-title: "Exploring and Analyzing LC-MS data with Spectra and xcms"
+title: "Exploring and Analyzing LC-MS Data with Spectra and xcms"
 author:
 - name: "Philippine Louail, Johannes Rainer"
   affiliation: "Eurac Research, Bolzano, Italy; johannes.rainer@eurac.edu github: jorainer"
@@ -210,8 +210,8 @@ spectra(data)
 
 ```
 
-The new version of *xcms* uses thus the more modern and flexible infrastructure
-for MS data analysis provided by the `r Biocpkg("Spectra")` package. While it is
+From version 4 on, *xcms* uses the more modern and flexible infrastructure for
+MS data analysis provided by the `r Biocpkg("Spectra")` package. While it is
 still possible and supported to use *xcms* together with the `r
 Biocpkg("MSnbase")` package, users are advised to switch to this new
 infrastructure as it provides more flexibility and a higher performance. Also,
@@ -252,6 +252,10 @@ fromFile(data) |>
     table()
 ```
 
+Such basic data summaries can be helpful for a first initial quality assessment
+to potentially identify problematic data files with e.g. a unexpected low number
+of spectra.
+
 Besides the peak data (*m/z* and intensity values) also additional spectra
 variables (metadata) are available in a `Spectra` object. These can be listed
 using the `spectraVariables` function that we call on our example MS data below.
@@ -278,10 +282,7 @@ spectra(data) |>
     table()
 ```
 
-The present data set contains thus 1,862 spectra, all from MS level 1. Such
-basic data summaries can be helpful for a first initial quality assessment to
-potentially identify problematic data files with e.g. a unexpected low number of
-spectra.
+The present data set contains thus 1,862 spectra, all from MS level 1.
 
 We could also check the number of peaks per spectrum in the different data
 files. The number of peaks per spectrum can be extracted with the `lengths`
@@ -736,8 +737,9 @@ fls <- basename(fls)
 data <- readMsExperiment(fls, sampleData = pd)
 ```
 
-This, or similar, code would allow to create scripts to batch-perform an R-based
-centroiding.
+Thus, with few lines of R code we performed MS data centroiding in R which gives
+us possibly more, and better, control over the process and would also allow
+(parallel) batch processing.
 
 
 
@@ -745,11 +747,11 @@ centroiding.
 
 Preprocessing of (untargeted) LC-MS data aims at detecting and quantifying the
 signal from ions generated from all molecules present in a sample. It consists
-of the following 3 steps: chromatographic peak detection, alignment (also
-called retention time correction) and correspondence (also called peak
-grouping). The resulting matrix of feature abundances can then be used as an
-input in downstream analyses including data normalization, identification of
-features of interest and annotation of features to metabolites.
+of the following 3 steps: chromatographic peak detection, retention time
+alignment and correspondence (also called peak grouping). The resulting matrix
+of feature abundances can then be used as an input in downstream analyses
+including data normalization, identification of features of interest and
+annotation of features to metabolites.
 
 
 ## Chromatographic peak detection
@@ -891,10 +893,11 @@ plot(srn)
 We can observe some scattering of the data points around an *m/z* of 105.05 in
 the lower panel of the above plot. This scattering also decreases with
 increasing signal intensity (as for many MS instruments the precision of the
-signal increases with the intensity). To investigate the observed differences in
-*m/z* values for the signal of serine we below first subset the data to the
-first file and then restrict the *m/z* range further to values between 106.045
-and 106.055.
+signal increases with the intensity). To quantify the observed differences in
+*m/z* values for the signal of serine we restrict the data to a *bona fide*
+region with signal for the serine ion. Below we first subset the data to the
+first file and then restrict the *m/z* range to values between 106.045 and
+106.055.
 
 ```{r}
 #' Reduce the data set to signal of the [M+H]+ ion of serine
@@ -1054,14 +1057,15 @@ observed above (see also the documentation of the `refineChromPeaks` function
 for all possible refinement options).
 
 To fuse the wrongly split peaks in the second row, we use the
-`MergeNeighboringPeaksParam` algorithm and configure it to merge all
-chromatographic peaks with a similar *m/z* that are less than 8 seconds apart
-from each other on the retention time axis (parameter `expandRt = 4`; the
-distance tail to head of the peaks evaluated for merging should thus be less
-than `2 * expandRt`) and for which the signal (intensity) between the two peaks
-is higher than 75% of the smaller apex intensity of the two peaks (parameter
-`minProp = 0.75`). We below apply these settings on the EICs and evaluate the
-result of this post-processing.
+`MergeNeighboringPeaksParam` algorithm that merges chromatographic peaks that
+are overlapping on the *m/z* and retention time dimension for which the signal
+between them is lower than a certain value. We specify `expandRt = 4` to expand
+the retention time width of each peak by 4 seconds on each side and set `minProp
+= 0.75`. All chromatographic peaks with a distance tail to head in retention
+time dimension that is less `2 * expandRt` and for which the intensity between
+them is higher than 75% of the lower (apex) intensity of the two peaks are thus
+merged. We below apply these settings on the EICs and evaluate the result of
+this post-processing.
 
 ```{r}
 #' Define the setting for the peak refinement
@@ -1085,7 +1089,7 @@ data <- refineChromPeaks(data, param = mpp)
 ```
 
 
-## Alignment
+## Retention time alignment
 
 While chromatography helps to better discriminate between analytes it is also
 affected by variances that lead to shifts in retention times between measurement

From 9242e2da7955ef7644938b6eb6911b782e454a51 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Thu, 5 Oct 2023 11:38:58 +0200
Subject: [PATCH 20/42] Bump version

---
 DESCRIPTION | 2 +-
 NEWS.md     | 5 +++++
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index b33e7fc..aaabce7 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: xcmsTutorials
 Title: Exploring and Analyzing LC-MS data with Spectra and xcms
-Version: 1.0.0
+Version: 1.0.1
 Authors@R: c(
 	     person(given = "Johannes", family = "Rainer",
 		    email = "Johannes.Rainer@eurac.edu",
diff --git a/NEWS.md b/NEWS.md
index 2177f4c..94b8362 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,10 @@
 # xcmsTutorials 1.0
 
+## Changes in 1.0.1
+
+- Small fixes with bullet points in the README and corrections/reformulations in
+  the workshop Rmd.
+
 ## Changes in 1.0.0
 
 - Restructure and clarify basic data access section.

From 69d0c19efd983493c2f4a8f60b2f6dd90f9ae59f Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Fri, 6 Oct 2023 14:29:30 +0200
Subject: [PATCH 21/42] Tiny rewording

---
 vignettes/xcms-preprocessing.Rmd | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index 514c9d3..cd16c4b 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -689,9 +689,9 @@ spectra(data) <- sps_cent
 
 #' Plot the centroided data for Serine
 data |>
-filterRt(rt = c(175, 189)) |>
-filterMz(mz = c(106.02, 106.07)) |>
-plot()
+    filterRt(rt = c(175, 189)) |>
+    filterMz(mz = c(106.02, 106.07)) |>
+    plot()
 ```
 
 The impact of the centroiding is clearly visible: each signal for an ion in a
@@ -1059,7 +1059,7 @@ for all possible refinement options).
 To fuse the wrongly split peaks in the second row, we use the
 `MergeNeighboringPeaksParam` algorithm that merges chromatographic peaks that
 are overlapping on the *m/z* and retention time dimension for which the signal
-between them is lower than a certain value. We specify `expandRt = 4` to expand
+between them is higher than a certain value. We specify `expandRt = 4` to expand
 the retention time width of each peak by 4 seconds on each side and set `minProp
 = 0.75`. All chromatographic peaks with a distance tail to head in retention
 time dimension that is less `2 * expandRt` and for which the intensity between
@@ -1105,8 +1105,8 @@ bpc_raw <- chromatogram(data, aggregationFun = "max", chromPeaks = "none")
 plot(bpc_raw, peakType = "none")
 ```
 
-While both samples were measured with the same setup in the same measurement
-run, slight drifts of the signal are visible. These were also already visible in
+Both samples were measured with the same setup in the same measurement run, but
+still small drifts of the signal are visible. These were also already visible in
 the EIC for serine, that we plot again below.
 
 ```{r}
@@ -1177,7 +1177,7 @@ data <- groupChromPeaks(data, pdp)
 
 This step now grouped chromatographic peaks across samples and defined so called
 LC-MS features (or simply features). We can thus now run the alignment using the
-*peakGroups* algorithm. The main parameter to define the hook peaks is (again)
+*peakGroups* algorithm. The main parameter to define the hook peaks is
 `minFraction`. Similar to the definition above, `minFraction` refers to the
 proportion of samples in which a chromatographic peak needs to be present. By
 setting `minFraction = 1` we base the alignment on features with peaks

From af2ad1f8dad478be55239c0d71a8473cfeaaa330 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Tue, 10 Oct 2023 15:00:54 +0200
Subject: [PATCH 22/42] Add installation instruction

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 8a6bc81..f609833 100644
--- a/README.md
+++ b/README.md
@@ -49,6 +49,8 @@ For on-line code evaluation, the workshop can also be run using a self-contained
 docker image with all R packages and a server version of RStudio (Posit)
 pre-installed:
 
+- If you don't already have, install [docker](https://www.docker.com/). Find
+  installation information [here](https://docs.docker.com/desktop/).
 - Get the [docker image](https://hub.docker.com/r/jorainer/xcms_tutorials) of
   this tutorial with `docker pull jorainer/xcms_tutorials:latest`.
 - Start docker using

From 16f19233e6f9331d97c43f29fffd7e662a5ec6e3 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Tue, 10 Oct 2023 15:05:07 +0200
Subject: [PATCH 23/42] Force installation of msdata

---
 DESCRIPTION | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index aaabce7..2c79320 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -24,12 +24,12 @@ Depends:
     R (>= 4.3),
     xcms (>= 3.99.4),
     Spectra (>= 1.11.10),
-    MsExperiment
+    MsExperiment,
+    msdata
 Suggests:
     MetaboCoreUtils,
     MsCoreUtils,
     png,
-    msdata,
     BiocStyle,
     knitr,
     rmarkdown,

From 124617691dd288f40076976b8328beeed58bee79 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Tue, 10 Oct 2023 15:18:32 +0200
Subject: [PATCH 24/42] Move msdata to depends

---
 DESCRIPTION | 2 +-
 NEWS.md     | 4 ++++
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 2c79320..d542685 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: xcmsTutorials
 Title: Exploring and Analyzing LC-MS data with Spectra and xcms
-Version: 1.0.1
+Version: 1.0.2
 Authors@R: c(
 	     person(given = "Johannes", family = "Rainer",
 		    email = "Johannes.Rainer@eurac.edu",
diff --git a/NEWS.md b/NEWS.md
index 94b8362..3dd6425 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,9 @@
 # xcmsTutorials 1.0
 
+## Changes in 1.0.2
+
+- Add the `msdata` to *depends* to ensure it's going to be installed.
+
 ## Changes in 1.0.1
 
 - Small fixes with bullet points in the README and corrections/reformulations in

From 91544f828f9f38bd251ca72c1a3e869d97e761a0 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Thu, 12 Oct 2023 07:36:33 +0200
Subject: [PATCH 25/42] Add reference to the MsQuality paper

---
 DESCRIPTION                      |  2 +-
 NEWS.md                          |  4 ++++
 vignettes/references.bib         | 18 ++++++++++++++++++
 vignettes/xcms-preprocessing.Rmd | 23 +++++++++++++----------
 4 files changed, 36 insertions(+), 11 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index d542685..aadd99b 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: xcmsTutorials
 Title: Exploring and Analyzing LC-MS data with Spectra and xcms
-Version: 1.0.2
+Version: 1.0.3
 Authors@R: c(
 	     person(given = "Johannes", family = "Rainer",
 		    email = "Johannes.Rainer@eurac.edu",
diff --git a/NEWS.md b/NEWS.md
index 3dd6425..e3bf8cb 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,9 @@
 # xcmsTutorials 1.0
 
+## Changes in 1.0.3
+
+- Add reference to the `MsQuality` paper.
+
 ## Changes in 1.0.2
 
 - Add the `msdata` to *depends* to ensure it's going to be installed.
diff --git a/vignettes/references.bib b/vignettes/references.bib
index b95f5e0..d490b4d 100644
--- a/vignettes/references.bib
+++ b/vignettes/references.bib
@@ -117,3 +117,21 @@ @article{gattoMSnbaseEfficientElegant2020a
   language = {eng},
   pmid = {32902283}
 }
+
+@article{naake_msquality_2023,
+	title = {{MsQuality} - an interoperable open-source package for the calculation of standardized quality metrics of mass spectrometry data},
+	issn = {1367-4811},
+	doi = {10.1093/bioinformatics/btad618},
+	abstract = {MOTIVATION: Multiple factors can impact accuracy and reproducibility of mass spectrometry data. There is a need to integrate quality assessment and control into data analytic workflows.
+RESULTS: The MsQuality package calculates 43 low-level quality metrics based on the controlled mzQC vocabulary defined by the HUPO-PSI on a single mass spectrometry-based measurement of a sample. It helps to identify low-quality measurements and track data quality. Its use of community-standard quality metrics facilitates comparability of quality assessment and control (QA/QC) criteria across datasets.
+AVAILABILITY: The R package MsQuality is available through Bioconductor at https://bioconductor.org/packages/MsQuality.
+SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
+	language = {eng},
+	journal = {Bioinformatics (Oxford, England)},
+	author = {Naake, Thomas and Rainer, Johannes and Huber, Wolfgang},
+	month = oct,
+	year = {2023},
+	pmid = {37812234},
+	keywords = {mass spectrometry, metabolomics, proteomics, quality control, R},
+	pages = {btad618},
+}
diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index cd16c4b..c977c8b 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -339,7 +339,7 @@ intensity(sp) |>
 
 The same operation can also be applied to the full data set. As an example we
 calculate below the total ion signal for each spectrum in the first file and
-determine the distribution of these using the `quantiles` function.
+determine the distribution of these using the `quantile` function.
 
 ```{r}
 #' Calculate the distribution of total ion signal of the first file
@@ -363,8 +363,8 @@ The total ion signals of the two data files is (as expected) similar. Through
 the `Spectra` object we have thus the possibility to inspect and explore the
 (raw) MS data of an experiment and use its functionality to create own quality
 assessment functions. Alternatively, also the `r Biocpkg("MsQuality")` package
-could be to calculate core MS quality metrics on a full experiment
-(`MsExperiment`) or individual data files (`Spectra`).
+[@naake_msquality_2023] could be to calculate core MS quality metrics on a full
+experiment (`MsExperiment`) or individual data files (`Spectra`).
 
 
 ## Data visualization
@@ -602,7 +602,7 @@ data |>
     filterRt(rt = c(175, 189)) |>
     filterMz(mz = serine_mz + c(-0.05, 0.05)) |>
     chromatogram() |>
-plot()
+    plot()
 ```
 
 The area of such a chromatographic peak is supposed to be proportional to the
@@ -636,7 +636,7 @@ focus on the mass peak for serine.
 #' Visualize the profile-mode mass peak for [M+H]+ of serine
 sps[1] |>
     filterMzRange(c(106.02, 106.07)) |>
-    plotSpectra()
+    plotSpectra(lwd = 2)
 abline(v = serine_mz, col = "#ff000080", lty = 3)
 ```
 
@@ -657,7 +657,7 @@ region in both data files.
 data |>
     filterRt(rt = c(175, 189)) |>
     filterMz(mz = c(106.02, 106.07)) |>
-    plot()
+    plot(cex = 2)
 ```
 
 The lower panel of the plot shows all mass peaks measured by the instrument:
@@ -691,7 +691,7 @@ spectra(data) <- sps_cent
 data |>
     filterRt(rt = c(175, 189)) |>
     filterMz(mz = c(106.02, 106.07)) |>
-    plot()
+    plot(cex = 2)
 ```
 
 The impact of the centroiding is clearly visible: each signal for an ion in a
@@ -1455,7 +1455,8 @@ the definition of the features and display the first 6 rows
 
 ```{r correspondence-featureDefinitions}
 #' Definition of the features
-featureDefinitions(data) |> head()
+featureDefinitions(data) |>
+    head()
 ```
 
 Each row defines one feature and provides information on it's *m/z* (column
@@ -1480,7 +1481,8 @@ abundances and show the first 6 rows.
 
 ```{r}
 #' Get abundances for the first 6 features
-featureValues(data, method = "sum") |> head()
+featureValues(data, method = "sum") |>
+    head()
 ```
 
 We could now use this feature matrix for any downstream analysis. Such feature
@@ -1704,7 +1706,8 @@ simply use the `assay` function.
 
 ```{r}
 #' Get feature values
-assay(res) |> head()
+assay(res) |>
+    head()
 ```
 
 The `SummarizedExperiment` can be subset by rows and/or columns.

From 829c7eeddde6a196c3a429720475c8f0627e746a Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Thu, 12 Oct 2023 07:38:15 +0200
Subject: [PATCH 26/42] Remove cex = 2

---
 vignettes/xcms-preprocessing.Rmd | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index c977c8b..a6b1b53 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -657,7 +657,7 @@ region in both data files.
 data |>
     filterRt(rt = c(175, 189)) |>
     filterMz(mz = c(106.02, 106.07)) |>
-    plot(cex = 2)
+    plot()
 ```
 
 The lower panel of the plot shows all mass peaks measured by the instrument:
@@ -691,7 +691,7 @@ spectra(data) <- sps_cent
 data |>
     filterRt(rt = c(175, 189)) |>
     filterMz(mz = c(106.02, 106.07)) |>
-    plot(cex = 2)
+    plot()
 ```
 
 The impact of the centroiding is clearly visible: each signal for an ion in a

From bc97db08822540e06320250141d79eeb7de6a5dd Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Mon, 30 Oct 2023 10:16:52 +0100
Subject: [PATCH 27/42] Use Bioconductor 3.18

---
 DESCRIPTION | 2 +-
 Dockerfile  | 2 +-
 NEWS.md     | 4 ++++
 3 files changed, 6 insertions(+), 2 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index aadd99b..65430c7 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: xcmsTutorials
 Title: Exploring and Analyzing LC-MS data with Spectra and xcms
-Version: 1.0.3
+Version: 1.0.4
 Authors@R: c(
 	     person(given = "Johannes", family = "Rainer",
 		    email = "Johannes.Rainer@eurac.edu",
diff --git a/Dockerfile b/Dockerfile
index 2f9e7ae..eda5219 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,4 +1,4 @@
-FROM bioconductor/bioconductor_docker:devel
+FROM bioconductor/bioconductor_docker:RELEASE_3_18
 
 LABEL name="jorainer/xcms_tutorials" \
       url="https://github.com/jorainer/xcmsTutorials" \
diff --git a/NEWS.md b/NEWS.md
index e3bf8cb..4c3da72 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,9 @@
 # xcmsTutorials 1.0
 
+## Changes in 1.0.4
+
+- Use the Bioconductor 3.18 release docker image.
+
 ## Changes in 1.0.3
 
 - Add reference to the `MsQuality` paper.

From 16dcb56899b4b24c53034d4c7f8b8bde30624117 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Tue, 5 Dec 2023 09:53:21 +0100
Subject: [PATCH 28/42] docs: add contribution guideline and CoC

---
 DESCRIPTION |  2 --
 README.md   | 10 ++++++++++
 2 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 65430c7..50a2d2f 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -35,8 +35,6 @@ Suggests:
     rmarkdown,
     RColorBrewer,
     SummarizedExperiment
-Remotes:
-    sneumann/xcms@jomain
 URL: https://jorainer.github.io/xcmsTutorials/
 BugReports: https://github.com/jorainer/xcmsTutorials/issues/new
 VignetteBuilder: knitr
diff --git a/README.md b/README.md
index f609833..5b3e4ce 100644
--- a/README.md
+++ b/README.md
@@ -77,6 +77,16 @@ BiocManager::install("jorainer/xcmsTutorials",
     dependencies = TRUE, ask = FALSE, update = TRUE)
 ```
 
+## Contribution
+
+For contributions, see the [RforMassSpectrometry contributions
+guideline](https://rformassspectrometry.github.io/RforMassSpectrometry/articles/RforMassSpectrometry.html#contributions).
+
+
+## Code of Conduct
+
+See the [RforMassSpectrometry Code of
+Conduct](https://rformassspectrometry.github.io/RforMassSpectrometry/articles/RforMassSpectrometry.html#code-of-conduct).
 
 
 ## Additional documentation resources and tutorials

From 94b34bc651e5f4bb986292da62989329b9fa638a Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Thu, 15 Feb 2024 08:52:19 +0100
Subject: [PATCH 29/42] Update the tutorial

- Adding an additional section on isotope identification.
---
 DESCRIPTION                      |   5 +-
 Dockerfile                       |   2 +-
 NEWS.md                          |   9 +
 README.md                        |  22 +-
 vignettes/xcms-preprocessing.Rmd | 863 ++++++++++++++++++++++---------
 5 files changed, 634 insertions(+), 267 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 50a2d2f..e86b82a 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: xcmsTutorials
 Title: Exploring and Analyzing LC-MS data with Spectra and xcms
-Version: 1.0.4
+Version: 1.1.0
 Authors@R: c(
 	     person(given = "Johannes", family = "Rainer",
 		    email = "Johannes.Rainer@eurac.edu",
@@ -34,7 +34,8 @@ Suggests:
     knitr,
     rmarkdown,
     RColorBrewer,
-    SummarizedExperiment
+    SummarizedExperiment,
+    pheatmap
 URL: https://jorainer.github.io/xcmsTutorials/
 BugReports: https://github.com/jorainer/xcmsTutorials/issues/new
 VignetteBuilder: knitr
diff --git a/Dockerfile b/Dockerfile
index eda5219..2f9e7ae 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,4 +1,4 @@
-FROM bioconductor/bioconductor_docker:RELEASE_3_18
+FROM bioconductor/bioconductor_docker:devel
 
 LABEL name="jorainer/xcms_tutorials" \
       url="https://github.com/jorainer/xcmsTutorials" \
diff --git a/NEWS.md b/NEWS.md
index 4c3da72..d5e8acf 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,14 @@
 # xcmsTutorials 1.0
 
+## Changes in 1.1.0
+
+- Use Bioconductor 3.19 devel release.
+- Add examples for simple quality assessment of BPC or BPS data.
+- Introduce parameter `ppm` for `PeakDensityParam` correspondence analysis
+  method.
+- Add an additional section for extraction of chromatographic or spectra data
+  for identified chromatographic peaks and perform isotopologue search in these.
+
 ## Changes in 1.0.4
 
 - Use the Bioconductor 3.18 release docker image.
diff --git a/README.md b/README.md
index 5b3e4ce..5b10cf8 100644
--- a/README.md
+++ b/README.md
@@ -9,12 +9,12 @@
 ![MsCoreUtils](man/figures/MsCoreUtils.png)
 
 This workshop provides an overview of recent developments in Bioconductor to
-work with mass spectrometry
+work with mass spectrometry data
 ([MsExperiment](https://github.com/RforMassSpectrometry/MsExperiment),
 [Spectra](https://github.com/RforMassSpectrometry/Spectra)) and specifically
 LC-MS data ([xcms](https://github.com/sneumann/xcms)) and walks through the
 preprocessing of a small data set emphasizing on selection of data-dependent
-settings for the individual pre-processing steps.
+settings for the individual preprocessing steps.
 
 Covered topics are:
 
@@ -45,9 +45,12 @@ generate the html file
 
 ## Installation
 
-For on-line code evaluation, the workshop can also be run using a self-contained
-docker image with all R packages and a server version of RStudio (Posit)
-pre-installed:
+The workshop files along with an R runtime environment including all required
+packages and the RStudio (Posit) editor are all bundled in a *docker*
+container. After installation, this docker container can be run on the computer
+and the code and examples from the workshop can be evaluated within this
+environment (without the need to install any additional packages or files). The
+required steps for installation are:
 
 - If you don't already have, install [docker](https://www.docker.com/). Find
   installation information [here](https://docs.docker.com/desktop/).
@@ -59,12 +62,12 @@ pre-installed:
       -e PASSWORD=bioc \
       -p 8787:8787 \
       jorainer/xcms_tutorials:latest
-  ```
+```
 
 - Enter `http://localhost:8787` in a web browser and log in with username
   `rstudio` and password `bioc`.
 - In the RStudio server version: open any of the R-markdown (*.Rmd*) files in
-  the *vignettes* folder and evaluate the R code blocks.
+  the *vignettes* folder and evaluate the R code blocks in that document.
 
 
 For manual installation, an R version >= 4.3.0 is required as well as recent
@@ -103,3 +106,8 @@ Conduct](https://rformassspectrometry.github.io/RforMassSpectrometry/articles/Rf
   https://rformassspectrometry.github.io/MetaboAnnotation/
 - Repository of the `CompoundDb` package:
   https://rformassspectrometry.github.io/CompoundDb/
+
+# Acknowledgments
+
+Thank you to [Philippine Louail](https://github.com/philouail) for fixing typos
+and suggesting improvements.
diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index a6b1b53..cff07d6 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -4,7 +4,7 @@ author:
 - name: "Philippine Louail, Johannes Rainer"
   affiliation: "Eurac Research, Bolzano, Italy; johannes.rainer@eurac.edu github: jorainer"
 graphics: yes
-date: "October 2023"
+date: "February 2024"
 output:
   BiocStyle::html_document:
     number_sections: true
@@ -15,11 +15,12 @@ vignette: >
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding[utf8]{inputenc}
   %\VignettePackage{xcmsTutorials}
-  %\VignetteDepends{xcms,Spectra,BiocStyle,MetaboCoreUtils,knitr,png,MsCoreUtils,msdata,MsExperiment}
+  %\VignetteDepends{xcms,Spectra,BiocStyle,MetaboCoreUtils,knitr,png,MsCoreUtils,msdata,MsExperiment,pheatmap}
 bibliography: references.bib
 ---
 
 ```{r style, message = FALSE, echo = FALSE, warning = FALSE, results = "asis"}
+#' Pre-loading libraries and define settings for rendering
 library("BiocStyle")
 library("knitr")
 library("rmarkdown")
@@ -60,10 +61,10 @@ across samples within an experiment. The resulting two-dimensional matrix with
 abundances of the so called *LC-MS features* in all samples can then be further
 processed, e.g. by normalizing the data to remove differences due to sample
 processing, batch effects or injection order-dependent signal drifts. LC-MS
-features are usually only characterized by their mass-to-charge ration (*m/z*)
+features are usually only characterized by their mass-to-charge ratio (*m/z*)
 and retention time and hence need to be annotated to the actual ions and
-metabolites they represent. Data normalization and annotation are not covered in
-here but links to related tutorials and workshops are provided at the
+metabolites they represent. Data normalization and annotation are not covered by
+this tutorial but links to related tutorials and workshops are provided at the
 end of the document.
 
 
@@ -151,6 +152,7 @@ an xls sheet that could then be imported with the `read_xlsx` function from the
 to the `readMsExperiment` call to import the data.
 
 ```{r load-data}
+#' Load required libraries
 library(xcms)
 library(MsExperiment)
 library(Spectra)
@@ -165,8 +167,8 @@ pd <- data.frame(file = basename(fls),
                  group = "POOL")
 
 #' Import the data of the experiment
-data <- readMsExperiment(fls, sampleData = pd)
-data
+mse <- readMsExperiment(fls, sampleData = pd)
+mse
 ```
 
 The MS data of the experiment is now *represented* by an `MsExperiment` object.
@@ -179,7 +181,7 @@ The `length` of an `MsExperiment` is defined by the number of samples (files)
 within the object.
 
 ```{r general-access}
-length(data)
+length(mse)
 
 ```
 
@@ -188,8 +190,8 @@ selected sample(s). To restrict to data from the second sample we use:
 
 ```{r}
 #' Subset the data
-data_2 <- data[2]
-data_2
+mse_2 <- mse[2]
+mse_2
 ```
 
 This did subset the full data, including sample information and spectra data
@@ -198,7 +200,7 @@ to those of the second file. Phenotype information can be retrieved with the
 
 ```{r}
 #' Extract sample information
-sampleData(data_2)
+sampleData(mse_2)
 ```
 
 The MS data is stored as a `Spectra` object within the `MsExperiment` and can be
@@ -206,18 +208,18 @@ accessed using the `spectra` function.
 
 ```{r show-fData}
 #' Access the MS data
-spectra(data)
+spectra(mse)
 
 ```
 
 From version 4 on, *xcms* uses the more modern and flexible infrastructure for
 MS data analysis provided by the `r Biocpkg("Spectra")` package. While it is
-still possible and supported to use *xcms* together with the `r
-Biocpkg("MSnbase")` package, users are advised to switch to this new
+still possible and supported to use *xcms* together with the
+`r Biocpkg("MSnbase")` package, users are advised to switch to this new
 infrastructure as it provides more flexibility and a higher performance. Also,
 through additional packages such as the `r Biocpkg("MsBackendRawFileReader")`,
-the new infrastructure would allow to import MS data from other files than mzML,
-mzXML or CDF files.
+the new infrastructure would allow to import MS data also from other files
+than mzML, mzXML or CDF files.
 
 In the next few examples we briefly explain the `Spectra` object and illustrate
 the use of such objects using some simple examples. More information on
@@ -233,7 +235,7 @@ that allows to concatenate consecutive calls in a more readable fashion.
 
 ```{r}
 #' Get the total number of spectra
-spectra(data) |>
+spectra(mse) |>
     length()
 ```
 
@@ -248,7 +250,7 @@ use that function to determine the total number of spectra for each sample.
 
 ```{r}
 #' Get the number of spectra per file.
-fromFile(data) |>
+fromFile(mse) |>
     table()
 ```
 
@@ -262,7 +264,7 @@ using the `spectraVariables` function that we call on our example MS data below.
 
 ```{r}
 #' List available spectra variables
-spectra(data) |>
+spectra(mse) |>
     spectraVariables()
 ```
 
@@ -277,7 +279,7 @@ different MS levels available in the object.
 
 ```{r}
 #' List number of spectra per MS level
-spectra(data) |>
+spectra(mse) |>
     msLevel() |>
     table()
 ```
@@ -291,9 +293,9 @@ the quartiles of the peak counts using the `quantile` function.
 
 ```{r}
 #' Get the distribution of peak counts per file
-spectra(data) |>
+spectra(mse) |>
     lengths() |>
-    split(fromFile(data)) |>
+    split(fromFile(mse)) |>
     lapply(quantile)
 
 ```
@@ -308,7 +310,7 @@ sample, extract the spectra from that sample and subset to the spectrum number
 
 ```{r}
 #' Extract one spectrum from the second file
-sp <- spectra(data[2])[123]
+sp <- spectra(mse[2])[123]
 sp
 ```
 
@@ -343,7 +345,7 @@ determine the distribution of these using the `quantile` function.
 
 ```{r}
 #' Calculate the distribution of total ion signal of the first file
-spectra(data[1]) |>
+spectra(mse[1]) |>
     intensity() |>
     sum() |>
     quantile()
@@ -353,7 +355,7 @@ We repeat the operation for the second file.
 
 ```{r}
 #' Repeat for the second file
-spectra(data[2]) |>
+spectra(mse[2]) |>
     intensity() |>
     sum() |>
     quantile()
@@ -363,8 +365,8 @@ The total ion signals of the two data files is (as expected) similar. Through
 the `Spectra` object we have thus the possibility to inspect and explore the
 (raw) MS data of an experiment and use its functionality to create own quality
 assessment functions. Alternatively, also the `r Biocpkg("MsQuality")` package
-[@naake_msquality_2023] could be to calculate core MS quality metrics on a full
-experiment (`MsExperiment`) or individual data files (`Spectra`).
+[@naake_msquality_2023] could be used to calculate core MS quality metrics on a
+full experiment (`MsExperiment`) or individual data files (`Spectra`).
 
 
 ## Data visualization
@@ -380,22 +382,80 @@ in a data file and allows thus to plot this information (on the y-axis) against
 the retention time for that spectrum. While we could also extract these values
 similarly to the total ion intensity in the previous section, we use below the
 `chromatogram` function that allows extraction of chromatographic data from MS
-data. With parameter `aggregationFun = "max"` we define to report the maximum
-signal per spectrum (setting `aggregationFun = "sum"` would in contrast sum up
-all intensities of a spectrum and hence create a TIC).
+data (e.g. from an `MsExperiment` object). With parameter `aggregationFun =
+"max"` we define to report the maximum signal per spectrum (setting
+`aggregationFun = "sum"` would in contrast sum up all intensities of a spectrum
+and hence create a TIC).
 
 ```{r}
 #' Extract and plot a BPC
-bpc <- chromatogram(data, aggregationFun = "max")
+bpc <- chromatogram(mse, aggregationFun = "max")
 plot(bpc)
 ```
 
 This plot shows the BPC for each of the two data files (each line representing
 one sample) and provides the information at what retention times signal was
 measured (thus at what retention times compounds eluted from the LC column). We
-can clearly spot regions along the retention time in which more compounds
+can clearly spot regions along the retention time in which more signal/compounds
 eluted. Also, the BPC of the two data files look similar, which is expected
-since both represent the same sample pool.
+since both represent the same sample.
+
+In addition to a visual inspection it is, especially for larger data sets,
+important to also *quantitatively* compare the data and hence derive quality
+metrics of a data set. For our base peak signals, however, retention times will
+be slightly different between the samples preventing thus a direct comparison
+and evaluation of the data. An easy solution to this is to *bin* the data into
+equal sized bins along the retention time axis and aggregate the measured
+intensities within each bin (per sample). Below we bin the data with a bin size
+of 1 second returning for each bin the maximal signal.
+
+```{r}
+#' Bin the BPC
+bpc_bin <- bin(bpc, binSize = 1)
+```
+
+After binning, the two chromatograms have the same retention times (and number
+of intensities) and we can e.g. combine the intensity vectors into a matrix of
+intensities.
+
+```{r}
+#' Create an intensity matrix
+bpc_mat <- do.call(cbind, lapply(bpc_bin, intensity))
+```
+
+We could now for example calculate the correlation between the intensities of
+the two samples, which can be used as a measure for the *similarity* of the
+LC-MS runs.
+
+```{r}
+#' Assess similarity between the numerical vectors using a simple
+#' Pearson correlation.
+cor(bpc_mat[, 1], bpc_mat[, 2])
+```
+
+For more than two samples a correlation matrix could be calculated:
+
+```{r}
+#' Create a pairwise correlation matrix
+cor(bpc_mat)
+```
+
+And such a correlation matrix could also be easily visualized as a *heatmap* -
+with the additional possibility to cluster samples with similar BPC. While for
+the present, two-sample data set, this is not very informative, for larger data
+sets it can help to evaluate differences between batches or to spot outlier
+samples (or rather LC-MS measurement runs).
+
+```{r, fig.cap = "Heatmap for similarity of the BPC of the two data files"}
+#' Create a heatmap of the correlation matrix
+cor(bpc_mat) |>
+    pheatmap()
+```
+
+This also exemplifies the power of an R-based analysis workflow that allows us
+to combine LC-MS specific analysis methods provided by the *xcms* package with
+build-in R functions or (statistical) data analysis methods provided by any
+other R package.
 
 The BPC collapsed the 3-dimensional LC-MS data (*m/z* by retention time by
 intensity) into 2 dimensions (retention time by intensity). An orthogonal
@@ -412,7 +472,7 @@ Below we plot the spectra for 2 consecutive scans.
 
 ```{r, fig.cap = "Spectra from two consecutive scan of the first file"}
 #' Plot two consecutive spectra
-plotSpectra(spectra(data)[123:124], xlim = c(105, 130))
+plotSpectra(spectra(mse)[123:124], xlim = c(105, 130))
 ```
 
 These two spectra could now be merged by reporting for each *m/z* (or rather for
@@ -422,19 +482,20 @@ aggregate/combine sets of spectra into a single spectrum. By default, this
 function will combine sets of spectra (that can be defined with parameter `f`)
 creating an union of the peaks present in spectra of a set. For mass peaks with
 a similar *m/z* value (depending on parameter `ppm`) the peaks' intensities are
-aggregated using the function defined with parameter `intensityFun` and only one
-peak is reported. With the setting below we combine all spectra from one file
-(by using `f = fromFile(data)`) into a single spectrum containing mass peaks
-present in any of the spectra of that file. Mass peaks with a difference in
-their *m/z* that is smaller than `ppm` (parts-per-million of the *m/z* value)
-are combined into one peak for which the maximal intensity of the grouped peaks
-is reported. Note that we set `ppm` to a rather small value because the present
-data is in profile mode.
+aggregated using the function defined with parameter `intensityFun` to result in
+a single value per (aggregated) peak. With the setting below we combine all
+spectra from one file (by using `f = fromFile(mse)`) into a single spectrum
+containing mass peaks present in any of the spectra of that file. Mass peaks
+with a difference in their *m/z* that is smaller than `ppm` (parts-per-million
+of the *m/z* value) are combined into one peak for which the maximal intensity
+of the grouped peaks is reported. Note that we set `ppm` to a rather small value
+because the present data is in profile mode. In general, it is suggested to use
+a rather small value for `ppm` for `combineSpectra` on MS1 data.
 
 ```{r}
 #' Combine all spectra of one file into a single spectrum
-bps <- spectra(data) |>
-    combineSpectra(f = fromFile(data), ppm = 5, intensityFun = max)
+bps <- spectra(mse) |>
+    combineSpectra(f = fromFile(mse), ppm = 5, intensityFun = max)
 bps
 ```
 
@@ -447,8 +508,32 @@ plotSpectra(bps)
 ```
 
 These BPS thus show the most common ions present in each of the two
-samples. Apparently there is quite some overlap in ion content between the two
-files.
+samples. Apparently there seems to be quite some overlap in ion content between
+the two files. Also here, we can calculate similarities between these
+spectra. As before, we could either bin the spectra and calculate a correlation
+matrix between their intensities:
+
+```{r}
+#' Bin the spectra and calculate similarity between their intensities
+bps_bin <- bin(bps, binSize = 0.01)
+
+do.call(cbind, intensity(bps_bin)) |>
+    cor()
+```
+
+Alternatively, we can also calculate the similarity between the base peak
+spectra using the `compareSpectra` function and one of the available peak
+similarity measures. Below we use the normalized dot product to calculate
+the similarity between the two spectra matching peaks using an *m/z* tolerance
+of 20 ppm.
+
+```{r}
+#' Calculate normalized dot product similarity between the spectra
+compareSpectra(bps, ppm = 20, FUN = MsCoreUtils::ndotproduct)
+```
+
+These measures thus allow us to get some general information on a data set and
+evaluate similarities between the samples.
 
 
 ### Detailed data inspection
@@ -465,7 +550,7 @@ between 180 and 181 seconds.
 
 ```{r}
 #' Extract all spectra measured between 180 and 181 seconds
-sps <- spectra(data) |>
+sps <- spectra(mse) |>
     filterRt(c(180, 181))
 sps
 ```
@@ -481,17 +566,20 @@ the originating sample, but that would involve additional R code.
 basename(dataOrigin(sps))
 ```
 
-Alternatively, we could use the `filterRt` function also directly on the
-`MsExperiment` which would subset the whole `MsExperiment` keeping hence the
-link between samples and spectra. Note however that only few filter and subset
-functions are at present available for `MsExperiment` objects while more are
-available for `Spectra` objects.
+Alternatively, we could use the `filterSpectra` function on the `MsExperiment`
+object passing the filter function (in our case `filterRt`) to that
+function. This filters the `Spectra` object *within* the `MsExperiment`
+retaining all associations (links) between samples and subset spectra. While
+some of the most commonly used filter functions, such as `filterRt` or
+`filterMsLevel`, are also implemented for `MsExperiment`, the `filterSpectra`
+function allows to apply any of the filter functions available for `Spectra`
+objects to the data.
 
 ```{r}
 #' Subset the whole MsExperiment
-data_sub <- filterRt(data, rt = c(180, 181))
+mse_sub <- filterSpectra(mse, filter = filterRt, rt = c(180, 181))
 #' Extract spectra from the subset for the first sample
-spectra(data_sub[1])
+spectra(mse_sub[1])
 ```
 
 For the present purpose it is however not important to keep the sample
@@ -547,12 +635,12 @@ serine_mz <- serine_mz[1, 1]
 We can now use this information to subset the MS data to the signal recorded for
 all ions with that particular *m/z*. We use again the `chromatogram` function
 and provide the *m/z* range of interest with the `mz` parameter of that
-function. Note that it would also be possible to first filter the data set by
+function. Note that alternatively we could also first filter the data set by
 *m/z* using the `filterMzRange` function and then extract the chromatogram.
 
 ```{r, fig.cap = "Ion trace for an ion of serine"}
 #' Extract a full RT chromatogram for ions with an m/z similar than serine
-serine_chr <- chromatogram(data, mz = serine_mz + c(-0.05, 0.05))
+serine_chr <- chromatogram(mse, mz = serine_mz + c(-0.01, 0.01))
 plot(serine_chr)
 ```
 
@@ -568,9 +656,10 @@ of pairs of retention time and intensity values of one sample) in a
 two-dimensional array, columns being samples (files) and rows data slices (i.e.,
 *m/z* - rt ranges). Note that this type of data representation, defined in the
 `r Biocpkg("MSnbase")` package, is likely to be replaced in future with a more
-efficient and flexible data structure similar to `Spectra`. Data from the
-individual chromatograms can be accessed using the `intensity` and `rtime`
-functions (similar to the `mz` and `intensity` functions for a `Spectra`
+efficient and flexible data structure similar to `Spectra`.
+
+Data from the individual chromatograms can be accessed using the `intensity` and
+`rtime` functions (similar to the `mz` and `intensity` functions for a `Spectra`
 object).
 
 ```{r chromatogram}
@@ -585,7 +674,7 @@ rtime(serine_chr[1, 1]) |>
 
 Note that an `NA` is reported if in the *m/z* range from which the
 chromatographic data was extracted no intensity was measured at the given
-retention time.
+retention time (i.e. in a spectrum).
 
 At last we further focus on the tentative signal of serine extracting the ion
 chromatogram restricting on the retention time range containing its
@@ -598,9 +687,9 @@ extracted ion chromatogram (EIC, sometimes also referred to as XIC) for the
 
 ```{r, fig.cap = "Extracted ion chromatogram for serine."}
 #' Create an EIC for serine
-data |>
+mse |>
     filterRt(rt = c(175, 189)) |>
-    filterMz(mz = serine_mz + c(-0.05, 0.05)) |>
+    filterMzRange(mz = serine_mz + c(-0.01, 0.01)) |>
     chromatogram() |>
     plot()
 ```
@@ -654,9 +743,9 @@ region in both data files.
 
 ```{r serine-profile-mode-data, fig.cap = "Profile data for Serine."}
 #' Visualize the full MS data for a small m/z - rt area
-data |>
+mse |>
     filterRt(rt = c(175, 189)) |>
-    filterMz(mz = c(106.02, 106.07)) |>
+    filterMzRange(mz = c(106.02, 106.07)) |>
     plot()
 ```
 
@@ -675,22 +764,22 @@ Next, we *smooth* the data in each spectrum using a Savitzky-Golay filter, which
 usually improves data quality by reducing noise. Subsequently we perform the
 centroiding of the data based on a simple peak-picking strategy that reports the
 maximum signal for each mass peak in each spectrum. Finally, we replace the
-spectra in data (`MsExperiment`) object with the centroided spectra and
+spectra in the data (`MsExperiment`) object with the centroided spectra and
 visualize the result repeating the visualization from above.
 
 ```{r centroiding, fig.cap = "Centroided data for Serine."}
 #' Smooth and centroid the spectra data
-sps_cent <- spectra(data) |>
+sps_cent <- spectra(mse) |>
     smooth(method = "SavitzkyGolay", halfWindowSize = 6L) |>
     pickPeaks(halfWindowSize = 2L)
 
-#' Replace spectra
-spectra(data) <- sps_cent
+#' Replace spectra in the original data object
+spectra(mse) <- sps_cent
 
 #' Plot the centroided data for Serine
-data |>
+mse |>
     filterRt(rt = c(175, 189)) |>
-    filterMz(mz = c(106.02, 106.07)) |>
+    filterMzRange(mz = c(106.02, 106.07)) |>
     plot()
 ```
 
@@ -706,7 +795,7 @@ can be used to export MS data.
 
 ```{r export-centroided-prepare, echo = FALSE, results = "hide"}
 #' Silently removing exported mzML files if they do already exist.
-lapply(basename(unique(dataOrigin(spectra(data)))), function (z) {
+lapply(basename(unique(dataOrigin(spectra(mse)))), function (z) {
     if (file.exists(z))
         file.remove(z)
 })
@@ -721,8 +810,8 @@ exported.
 
 ```{r export-centroided}
 #' Export the centroided data to new mzML files.
-export(spectra(data), backend = MsBackendMzR(),
-       file = basename(dataOrigin(spectra(data))))
+export(spectra(mse), backend = MsBackendMzR(),
+       file = basename(dataOrigin(spectra(mse))))
 
 ```
 
@@ -734,15 +823,14 @@ and proceed with the analysis.
 fls <- basename(fls)
 
 #' Read the centroided data.
-data <- readMsExperiment(fls, sampleData = pd)
+mse <- readMsExperiment(fls, sampleData = pd)
 ```
 
 Thus, with few lines of R code we performed MS data centroiding in R which gives
-us possibly more, and better, control over the process and would also allow
+us possibly more, and better, control over the process and enable also
 (parallel) batch processing.
 
 
-
 # Preprocessing of LC-MS data
 
 Preprocessing of (untargeted) LC-MS data aims at detecting and quantifying the
@@ -751,7 +839,9 @@ of the following 3 steps: chromatographic peak detection, retention time
 alignment and correspondence (also called peak grouping). The resulting matrix
 of feature abundances can then be used as an input in downstream analyses
 including data normalization, identification of features of interest and
-annotation of features to metabolites.
+annotation of features to metabolites. In the following sections we perform such
+preprocessing of our test data set, adapting the settings for the preprocessing
+algorithms to our data.
 
 
 ## Chromatographic peak detection
@@ -762,9 +852,9 @@ identifying and quantifying such signals as shown in the sketch below.
 
 ![Chromatographic peak detection](images/LCMS-data-peaks.png)
 
-Such peak detection can be performed with the *xcms* package using its
-`findChromPeaks` function. Several peak detection algorithms are available that
-can be selected and configured with their specific parameter objects:
+Such peak detection can be performed with the `r Biocpkg("xcms")` package using
+its `findChromPeaks` function. Several peak detection algorithms are available
+that can be selected and configured with their respective parameter objects:
 
 - `MatchedFilterParam` to perform peak detection as described in the original
   *xcms* article [@Smith:2006ic],
@@ -795,8 +885,8 @@ EIC for serine and run a *centWave*-based peak detection on that data using
 
 ```{r centWave-default}
 #' Get the EIC for serine in all files
-serine_chr <- chromatogram(data, rt = c(164, 200),
-                           mz = serine_mz + c(-0.05, 0.05),
+serine_chr <- chromatogram(mse, rt = c(164, 200),
+                           mz = serine_mz + c(-0.01, 0.01),
                            aggregationFun = "max")
 
 #' Get default centWave parameters
@@ -809,17 +899,18 @@ chromPeaks(res)
 
 The peak matrix returned by `chromPeaks` is empty, thus, with the default
 settings *centWave* failed to identify any chromatographic peak in the EIC for
-serine. These default values are shown below.
+serine. These default values are shown below:
 
 ```{r centWave-default-parameters}
+#' Default centWave parameters
 cwp
 ```
 
-Particularly the settings for `peakwidth` does not fit our data. The default for
+Particularly the setting for `peakwidth` does not fit our data. The default for
 this parameter expects chromatographic peaks between 20 and 50 seconds
 wide. When we plot the extracted ion chromatogram (EIC) for serine we can
-however see that these values are too large for the present data set (see
-below).
+however see that these values are way too large for our UHPLC-based data set
+(see below).
 
 ```{r, fig.cap = "Extracted ion chromatogram for serine."}
 plot(serine_chr)
@@ -828,15 +919,16 @@ plot(serine_chr)
 For serine, the chromatographic peak is about 5 seconds wide. We thus adapt the
 `peakwidth` for the present data set and repeat the peak detection using these
 settings. In general, the lower and upper peak width should be set to include
-most of the chromatographic peak widths. For the present data set we set the
-values to 2 to 10 seconds, i.e., to about half and two times the expected peak
-width. In addition, by setting `integrate = 2`, we select a different peak
-boundary estimation algorithm. This works particularly well for non-gaussian
-peak shapes and ensures that also signal from the peak's tail is integrated
-(eventually re-run the code with the default `integrate = 1` to compare the two
-approaches).
+most of the expected chromatographic peak widths. A good rule of thumb is to set
+it to about half to about twice the average expected peak width. For the present
+data set we thus set `peakwidth = c(2, 10)`. In addition, by setting `integrate
+= 2`, we select a different peak boundary estimation algorithm. This works
+particularly well for non-gaussian peak shapes and ensures that also signal from
+the peak's tail is integrated (eventually re-run the code with the default
+`integrate = 1` to compare the two approaches).
 
 ```{r centWave-adapted, fig.cap = "EIC for Serine with detected chromatographic peak", results = "hide"}
+#' Adapt centWave parameters
 cwp <- CentWaveParam(peakwidth = c(2, 10), integrate = 2)
 
 #' Run peak detection on the EIC
@@ -848,10 +940,11 @@ plot(serine_chr)
 
 Acceptable values for parameter `peakwidth` can thus be derived through visual
 inspection of EICs for ions known to be present in the sample (e.g. of internal
-standards). Ideally, this should be done for several compounds/ions. Tip:
+standards). Ideally, this should be done for several compounds/ions. *Tip*:
 ensure that the EIC contains also enough signal left and right of the actual
 chromatographic peak to allow *centWave* to properly estimate the background
-noise. Alternatively, reduce the value for the `snthresh` parameter.
+noise. Alternatively, or in addition, reduce the value for the `snthresh`
+parameter for peak detection performed on EICs.
 
 With our data set-specific `peakwidth` we were able to detect the peak for
 serine (highlighted in grey in the plot above). We can now use the `chromPeaks`
@@ -882,9 +975,9 @@ MS data for the data subset containing signal for serine.
 
 ```{r Serine-mz-scattering-plot}
 #' Restrict to data containing signal from serine
-srn <- data |>
+srn <- mse |>
     filterRt(rt = c(179, 186)) |>
-    filterMz(mz = c(106.04, 106.07))
+    filterMzRange(mz = c(106.04, 106.07))
 
 #' Plot the data
 plot(srn)
@@ -938,22 +1031,26 @@ The difference in *m/z* values for the serine data is thus between 0 and 27
 ppm. The maximum value could then be used for centWave's `ppm`
 parameter. Ideally, this should be evaluated for several ions and could be set
 to a value that allows to capture the full chromatographic peaks for most of the
-tested ions. We can next perform the peak detection on the full data set using
-our settings for the `ppm` and `peakwidth` parameters.
+tested ions. Also, the value for this parameter is generally much higher then
+the instrument precision (for the present instrument that would have been 5
+ppm). The value should thus be set to a value that allows/accepts some variance.
+
+We can next perform the peak detection on the full data set using our settings
+for the `ppm` and `peakwidth` parameters.
 
 ```{r findPeaks-centWave}
 #' Perform peak detection on the full data set
 cwp <- CentWaveParam(peakwidth = c(2, 10), ppm = 30, integrate = 2)
-data <- findChromPeaks(data, param = cwp)
+mse <- findChromPeaks(mse, param = cwp)
 ```
 
 The results form the chromatographic peak detection were added by the
-`findChromPeaks` to our `data` variable which now is an `XcmsExperiment` object
+`findChromPeaks` to our `mse` variable which now is an `XcmsExperiment` object
 that, by extending the `MsExperiment` class inherits all of its functionality
 and properties, but in addition contains also all *xcms* preprocessing results.
 
 ```{r}
-data
+mse
 ```
 
 We can extract the results from the peak detection step (as above) with the
@@ -964,7 +1061,7 @@ to 108 and a retention time from 150 to 190.
 
 ```{r}
 #' Access the peak detection results from a specific m/z - rt area
-chromPeaks(data, mz = c(106, 108), rt = c(150, 190))
+chromPeaks(mse, mz = c(106, 108), rt = c(150, 190))
 ```
 
 Again, each row in this matrix contains one identified chromatographic peak with
@@ -976,7 +1073,7 @@ samples (data files) the peak was identified.
 The chromatographic peak table above contains pairs of peaks with similar
 retention times and a difference in *m/z* values of about one. Together with the
 observed differences in intensities, this could indicate that one of the peaks
-represents the carbon 13 isotope and one the monoisotopic *main* peak. This is
+represents the carbon 13 isotope and one the monoisotopic compound. This is
 frequently observed in untargeted metabolomics.
 
 As a general overview of the peak detection results it can also be helpful to
@@ -985,7 +1082,7 @@ per sample. Below we count the number of peaks per sample.
 
 ```{r}
 #' Count peaks per file
-chromPeaks(data)[, "sample"] |>
+chromPeaks(mse)[, "sample"] |>
     table()
 ```
 
@@ -999,8 +1096,8 @@ data file using the `plotChromPeaks` function.
 ```{r plotChromPeaks, fig.cap = "Location of the identified chromatographic peaks in the *m/z* - rt space."}
 #' Plot the location of peaks in the m/z - rt plane
 par(mfrow = c(1, 2))
-plotChromPeaks(data, 1)
-plotChromPeaks(data, 2)
+plotChromPeaks(mse, 1)
+plotChromPeaks(mse, 2)
 ```
 
 Again, similar pattern are expected to be present for the two data files.
@@ -1008,15 +1105,16 @@ Again, similar pattern are expected to be present for the two data files.
 After chromatographic peak detection it is generally a good idea to visually
 inspect individual chromatographic peaks and evaluate the performance of the
 peak detection step. This could be done by plotting EICs of known compounds/ions
-in the data or by randomly select chromatographic peaks. *m/z* - retention time
-regions for random peaks could be defined using the example code below.
+in the data or by randomly selecting chromatographic peaks. *m/z* - retention
+time regions for random peaks could be defined using the example code below.
 
 ```{r}
 #' Select 4 random peaks
-idx <- sample(seq_len(nrow(chromPeaks(data))), 4)
+npeaks <- nrow(chromPeaks(mse))
+idx <- sample(seq_len(npeaks), 4)
 
 #' Extract m/z-rt regions for 4 random peaks
-mz_rt <- chromPeaks(data)[idx, c("rtmin", "rtmax", "mzmin", "mzmax")]
+mz_rt <- chromPeaks(mse)[idx, c("rtmin", "rtmax", "mzmin", "mzmax")]
 
 #' Expand the rt range by 10 seconds on both sides
 mz_rt[, "rtmin"] <- mz_rt[, "rtmin"] - 10
@@ -1025,6 +1123,8 @@ mz_rt[, "rtmax"] <- mz_rt[, "rtmax"] + 10
 mz_rt[, "mzmin"] <- mz_rt[, "mzmin"] - 0.005
 mz_rt[, "mzmax"] <- mz_rt[, "mzmax"] + 0.005
 
+#' Display the randomly selected regions
+mz_rt
 ```
 
 For our example we however manually define *m/z* - retention time regions
@@ -1041,7 +1141,7 @@ mz_rt <- rbind(c(106.045, 106.055, 165, 195),
                c(105.468, 105.478, 190, 215))
 
 #' Extract the EICs
-eics <- chromatogram(data, mz = mz_rt[, 1:2], rt = mz_rt[, 3:4], )
+eics <- chromatogram(mse, mz = mz_rt[, 1:2], rt = mz_rt[, 3:4], )
 #' Plot the EICs
 plot(eics)
 ```
@@ -1049,23 +1149,23 @@ plot(eics)
 While the peak detection worked nicely for the signals in the upper row, it
 failed to define chromatographic peaks containing the full signal in the lower
 row. In both cases, the signal was split into separate chromatographic peaks
-within the same sample. This is a common problem with *centWave* on noisy and
-broad signals. We could either try to adapt the *centWave* settings and repeat
-the chromatographic peak detection or use the `refineChromPeaks` function that
-allows to post-process peak detection results and fix problems such as those
-observed above (see also the documentation of the `refineChromPeaks` function
-for all possible refinement options).
+within the same sample. This is a common problem with *centWave* on such noisy
+and broad signals. We could either try to adapt the *centWave* settings and
+repeat the chromatographic peak detection or use the `refineChromPeaks` function
+that allows to post-process peak detection results and fix such problems (see
+also the documentation of the `refineChromPeaks` function for all possible
+refinement options).
 
 To fuse the wrongly split peaks in the second row, we use the
 `MergeNeighboringPeaksParam` algorithm that merges chromatographic peaks that
 are overlapping on the *m/z* and retention time dimension for which the signal
 between them is higher than a certain value. We specify `expandRt = 4` to expand
 the retention time width of each peak by 4 seconds on each side and set `minProp
-= 0.75`. All chromatographic peaks with a distance tail to head in retention
-time dimension that is less `2 * expandRt` and for which the intensity between
-them is higher than 75% of the lower (apex) intensity of the two peaks are thus
-merged. We below apply these settings on the EICs and evaluate the result of
-this post-processing.
+= 0.75`. All chromatographic peaks with a distance tail-to-head in retention
+time dimension that is less than `2 * expandRt` and for which the intensity
+between them is higher than 75% of the lower (apex) intensity of the two peaks
+are thus merged. We below apply these settings on the EICs and evaluate the
+result of this post-processing.
 
 ```{r}
 #' Define the setting for the peak refinement
@@ -1078,14 +1178,35 @@ eics <- refineChromPeaks(eics, param = mpp)
 plot(eics)
 ```
 
-The peak post-processing was able to fuse the signal for the neighboring peaks
+The peak post-processing was able to merge the signal for the neighboring peaks
 in the lower panel, while keeping the peaks for the different isomers present in
 the top right plot separate. We next apply this same peak refinement on the full
 data set.
 
 ```{r}
 #' Perform peak refinement on the full data set
-data <- refineChromPeaks(data, param = mpp)
+mse <- refineChromPeaks(mse, param = mpp)
+```
+
+The number of peaks per sample after peak refinement is shown below.
+
+```{r}
+chromPeaks(mse)[, "sample"] |>
+    table()
+```
+
+Also, `refineChromPeaks` adds information on the peak refinement to the object's
+`chromPeakData` data frame which provides additional metadata information for
+each chromatographic peak:
+
+```{r}
+chromPeakData(mse)
+```
+
+And the number of merged peaks is thus:
+
+```{r}
+sum(chromPeakData(mse)$merged)
 ```
 
 
@@ -1101,7 +1222,7 @@ peaks.
 
 ```{r alignment-bpc-raw, fig.cap = "BPC of all files."}
 #' Extract base peak chromatograms
-bpc_raw <- chromatogram(data, aggregationFun = "max", chromPeaks = "none")
+bpc_raw <- chromatogram(mse, aggregationFun = "max", chromPeaks = "none")
 plot(bpc_raw, peakType = "none")
 ```
 
@@ -1127,7 +1248,7 @@ one of the available alignment algorithms, that can be selected, and configured,
 with the respective parameter objects:
 
 - `PeakGroupsParam`: the *peakGroups* [@Smith:2006ic] method aligns samples
-  based on the retention times of a set of so called *hook peaks* (or
+  based on the retention times of a set of so called *anchor peaks* (or
   housekeeping peaks) in the different samples of an experiment. These peaks are
   supposed to represent signal from ions expected to be present in most of the
   samples of an experiment and the method aligns these samples by minimizing the
@@ -1146,22 +1267,24 @@ experiment based on these. See the alignment section in the *xcms*
 [vignette](https://bioconductor.org/packages/release/bioc/vignettes/xcms/inst/doc/xcms.html)
 for more information on this subset-based alignment. Note that such a
 subset-based alignment requires the samples to be loaded in the order in which
-they were measured.
+they were measured. Also, recently, functionality was added to *xcms* to perform
+the alignment on pre-selected signals (e.g. retention times of internal
+standards) or to align a data set against an external reference.
 
 For our example we use the *peakGroups* method that, as mentioned above, aligns
-samples based on the retention times of *hook peaks* (or housekeeping peaks). To
-define these, we need to first run an initial correspondence analysis to group
-chromatographic peaks across samples. Below we use the *peakDensity* method for
-correspondence. Details about this method and explanations on the choices of its
-parameters are provided in the next section. In brief, parameter `sampleGroups`
-defines to which sample group of the experiment individual samples belong to,
-and parameter `minFraction` specifies the proportion of samples (of one sample
-group) in which a chromatographic peak needs to be identified (for a particular
-*m/z* - retention time region) to group them into an LC-MS feature. For our
-example we use the sample group definition in the `sampleData` of our `data`
-variable and set `minFraction = 1` requiring thus a chromatographic peak to be
-identified in 100% of available samples to define a feature. Generally, if
-correspondence is performed on more heterogeneous samples `minFraction` values
+samples based on the retention times of *anchor peaks*. To define these, we need
+to first run an initial correspondence analysis to group chromatographic peaks
+across samples. Below we use the *peakDensity* method for correspondence
+(details about this method and explanations on the choices of its parameters are
+provided in the next section). In brief, parameter `sampleGroups` defines to
+which sample group of the experiment individual samples belong to, and parameter
+`minFraction` specifies the proportion of samples (of one sample group) in which
+a chromatographic peak needs to be identified (for a particular *m/z* -
+retention time region) to group them into an LC-MS feature. For our example we
+use the sample group definition in the `sampleData` of our `mse` variable and
+set `minFraction = 1` requiring thus a chromatographic peak to be identified in
+all (100%) of available samples to define a feature. Generally, if
+correspondence is performed on more heterogeneous samples, `minFraction` values
 between 0.6 and 0.8 could be used instead. Since the aim of this initial
 correspondence is to define some (presumably well separated) groups of
 chromatographic peaks across the samples, its settings does not need to be fully
@@ -1170,14 +1293,14 @@ optimized.
 ```{r}
 #' Define the settings for the initial peak grouping - details for
 #' choices in the next section.
-pdp <- PeakDensityParam(sampleGroups = sampleData(data)$group, bw = 1.8,
-                        minFraction = 1, binSize = 0.02)
-data <- groupChromPeaks(data, pdp)
+pdp <- PeakDensityParam(sampleGroups = sampleData(mse)$group, bw = 1.8,
+                        minFraction = 1, binSize = 0.02, ppm = 10)
+mse <- groupChromPeaks(mse, pdp)
 ```
 
 This step now grouped chromatographic peaks across samples and defined so called
 LC-MS features (or simply features). We can thus now run the alignment using the
-*peakGroups* algorithm. The main parameter to define the hook peaks is
+*peakGroups* algorithm. The main parameter to define the anchor peaks is
 `minFraction`. Similar to the definition above, `minFraction` refers to the
 proportion of samples in which a chromatographic peak needs to be present. By
 setting `minFraction = 1` we base the alignment on features with peaks
@@ -1187,18 +1310,39 @@ sample pools) values `>= 0.9` can be used. Otherwise, values between 0.7 and 0.9
 might be more advisable to ensure that a reasonable set of features are
 selected.
 
-After having identified the features that should be used as *hook peaks* the
-algorithm minimizes the observed between-sample retention time differences for
-these. Parameter `span` defines the degree of smoothing of the loess function
-that is used to allow different regions along the retention time axis to be
-adjusted by a different factor. A value of 0 will most likely cause overfitting,
-while 1 would cause all retention times of a sample to be shifted by a constant
-value. Values between 0.4 and 0.6 seem to be reasonable for most experiments.
+To evaluate anchor peaks that would be selected based the defined settings, we
+can use the `adjustRtimePeakGroups` method:
+
+```{r}
+#' Get the anchor peaks that would be selected
+pgm <- adjustRtimePeakGroups(mse, PeakGroupsParam(minFraction = 1))
+head(pgm)
+```
+
+Ideally, if possible, the anchor peaks should span a large range of the
+retention time range to allow alignment of the full LC runs. Below evaluate the
+distribution of retention times of the anchor peaks in the first sample.
+
+```{r}
+#' Evaluate distribution of anchor peaks' rt in the first sample
+quantile(pgm[, 1])
+```
+
+Anchor peaks cover thus most of the retention time range.
+
+After having identified the features that should be used as anchor peaks (based
+on the `minFraction` parameter) the algorithm minimizes the observed
+between-sample retention time differences for these. Parameter `span` defines
+the degree of smoothing of the loess function that is used to allow different
+regions along the retention time axis to be adjusted by a different factor. A
+value of 0 will most likely cause overfitting, while 1 would cause all retention
+times of a sample to be shifted by a constant value. Values between 0.4 and 0.6
+seem to be reasonable for most experiments.
 
 ```{r alignment-correspondence}
 #' Define settings for the alignment
 pgp <- PeakGroupsParam(minFraction = 1, span = 0.6)
-data <- adjustRtime(data, param = pgp)
+mse <- adjustRtime(mse, param = pgp)
 ```
 
 After an alignment it is suggested to evaluate its results using the
@@ -1212,18 +1356,18 @@ minimized the difference in retention times).
 
 ```{r alignment-result, fig.cap = "Alignment results: differences between raw and adjusted retention times for each sample."}
 #' Plot the difference between raw and adjusted retention times
-plotAdjustedRtime(data)
+plotAdjustedRtime(mse)
 grid()
 ```
 
 As a rule of thumb, the differences between raw and adjusted retention times in
-the plot above should be reasonable. Also, if possible, hook peaks should be
-present along a wide span of the retention time range, to avoid the need for
-extrapolation (which usually results in a too strong adjustment). For our
-example, the largest adjustments are between 1 and 2 seconds, which is
-reasonable given that the two samples were measured during the same measurement
-run. Also, features used for the alignment (i.e. hook peaks) are spread across
-the full retention time range.
+the plot above should be reasonable. Also, if possible, anchor peaks (indicated
+with black points in the plot above) should be present along a wide span of the
+retention time range, to avoid the need for extrapolation (which usually results
+in a too strong adjustment). For our example, the largest adjustments are
+between 1 and 2 seconds, which is reasonable given that the two samples were
+measured during the same measurement run. Also, features used for the alignment
+(i.e. anchor peaks) are spread across the full retention time range.
 
 To evaluate the impact of the alignment we next also plot the BPC before and
 after alignment. In a similar way as before, we set `chromPeaks = "none"` in the
@@ -1239,7 +1383,8 @@ plot(bpc_raw)
 grid()
 
 #' Plot the BPC after alignment
-plot(chromatogram(data, aggregationFun = "max", chromPeaks = "none"))
+chromatogram(mse, aggregationFun = "max", chromPeaks = "none") |>
+    plot()
 grid()
 ```
 
@@ -1255,8 +1400,8 @@ par(mfrow = c(1, 2), mar = c(4, 4.5, 1, 0.5))
 plot(serine_chr)
 grid()
 #' EIC after alignment
-serine_chr_adj <- chromatogram(data, rt = c(164, 200),
-                               mz = serine_mz + c(-0.05, 0.05),
+serine_chr_adj <- chromatogram(mse, rt = c(164, 200),
+                               mz = serine_mz + c(-0.01, 0.01),
                                aggregationFun = "max")
 plot(serine_chr_adj)
 grid()
@@ -1299,7 +1444,7 @@ parameter objects:
 Both methods group chromatographic peaks from different samples with similar
 *m/z* and retention times into features. For our example we use the *peak
 density* method. This algorithm iterates through small slices along the *m/z*
-dimension and groups within each slice chromatographic peaks with similar
+dimension and groups, within each slice, chromatographic peaks with similar
 retention times. The grouping depends on the distribution (density) of
 chromatographic peaks from all samples along the retention time axis. Peaks with
 similar retention time will result in a higher peak density at a certain
@@ -1384,41 +1529,58 @@ the peaks of the density curve are not considered for the grouping.
 
 By having defined a `bw` appropriate for our data set, we proceed and perform
 the correspondence analysis on the full data set. Other parameters of
-*peakDensity* are `binSize` and `minFraction`. The former defines the *m/z*
-widths of the slices along the *m/z* dimension the algorithm will iterate
-through. This value depends on the resolution (and noise) of the instrument, and
-should not be set to a too small value, but also not too large (to avoid peaks
-from different ions, with slightly different *m/z* but similar retention times,
-to be grouped into the same feature). The `minFraction` parameter (already
-discussed above) defines the proportion of samples within at least one sample
-group in which chromatographic peaks need to be identified in order to define a
-feature. For our example we use a `binSize = 0.02` hence grouping
-chromatographic peaks, with similar retention time, and with a difference in
-their *m/z* that is smaller than 0.02 into the same feature and `minFraction =
-0.4` thus defining features for chromatographic peak that were identified in at
-least 50% of samples per sample group.
+*peakDensity* are `binSize` and `minFraction`. The `minFraction` parameter
+(already discussed above) defines the proportion of samples within at least one
+sample group in which chromatographic peaks need to be identified in order to
+define a feature.
+
+`binSize` defines the *m/z* widths of the slices along the *m/z* dimension the
+algorithm will iterate through. This parameter thus translates into the maximal
+acceptable difference in *m/z* values for peaks to be considered representing
+signal from the same ion. The value depends on the resolution (and noise) of the
+instrument, and should not be set to a too small value, but also not too large
+(to avoid peaks from different ions, with slightly different *m/z* but similar
+retention times, to be grouped into the same feature). Note that by default a
+**constant** *m/z* width is used, which might however not reflect the
+*m/z*-dependent measurement error of some instruments (such as TOF
+instruments). To address this, the parameter `ppm` was recently added that
+allows to generate *m/z*-dependent bin sizes: the width of the *m/z* slices
+increases by `ppm` of the bin's *m/z* along the *m/z* axis.
+
+For our correspondence analysis we set the maximal acceptable difference of
+chrom peaks' *m/z* values with `binSize = 0.02` and `ppm = 10`, hence grouping
+chromatographic peaks with similar retention time and with a difference of their
+*m/z* values that is smaller than 0.02 + 10 ppm of their *m/z* values. By
+setting `minFraction = 0.4` we in addition require for a feature that a
+chromatographic peak was detected in `>=` 40% of samples of at least one sample
+group.
 
 ```{r correspondence-analysis}
-#' Set optimized settings
-pdp <- PeakDensityParam(sampleGroups = sampleData(data)$group, bw = 1.8,
-                        minFraction = 0.4, binSize = 0.02)
+#' Set in addition parameter ppm to a value of 10
+pdp <- PeakDensityParam(sampleGroups = sampleData(mse)$group, bw = 1.8,
+                        minFraction = 0.4, binSize = 0.02, ppm = 10)
 
 #' Perform the correspondence analysis on the full data
-data <- groupChromPeaks(data, param = pdp)
-data
+mse <- groupChromPeaks(mse, param = pdp)
+mse
 ```
 
+The present data set is restricted to a quite narrow *m/z* range, thus, the
+parameter `ppm` does not have a strong impact. For *real* data sets, this
+parameter results in an *m/z*-dependent *m/z* width of detected features. See
+also the [*xcms vignette*](LLLLL) for an example.
+
 Over 300 features were identified in our example data set. Again, it is
 suggested to evaluate the results on selected compounds/ions. We therefore
 extract below the chromatogram for the *m/z* range containing signals for
-betaine and valine. After a correspondence analysis also features are extracted
-by the `chromatogram` call and we can show the results from the actual
+betaine and valine. After a correspondence analysis also feature definitions are
+extracted by the `chromatogram` call and we can show the results from the actual
 correspondence analysis (based also on the settings that were used) by setting
 `simulate = FALSE` in the `plotChromPeakDensity` call.
 
-```{r correspondence-evaluate, fig.cap = "Result of correspondence on a slice containing the isomers valine and betaine."}
+```{r correspondence-evaluate, fig.cap = "Result of correspondence on an *m/z* slice containing the isomers valine and betaine."}
 #' Extract chromatogram including signal for betaine and valine
-chr_2 <- chromatogram(data, mz = 118.08625 + c(-0.01, 0.01),
+chr_2 <- chromatogram(mse, mz = 118.08625 + c(-0.01, 0.01),
                       aggregationFun = "max")
 #' Setting simulate = FALSE to show the actual correspondence results
 plotChromPeakDensity(chr_2, simulate = FALSE)
@@ -1427,9 +1589,9 @@ plotChromPeakDensity(chr_2, simulate = FALSE)
 We evaluate the results also on a different slice containing signal for ions
 from isomers leucine and isoleucine ([M+H]+ *m/z* 132.10191).
 
-```{r correspondence-evaluate-2, fig.cap = "Result of correspondence on a slice containing the isomers leucine and isoleucine."}
+```{r correspondence-evaluate-2, fig.cap = "Result of correspondence on an *m/z* slice containing the isomers leucine and isoleucine."}
 #' Extract chromatogram with signal for isomers leucine and isoleucine
-chr_3 <- chromatogram(data, mz = 132.10191 + c(-0.01, 0.01),
+chr_3 <- chromatogram(mse, mz = 132.10191 + c(-0.01, 0.01),
                       aggregationFun = "max")
 plotChromPeakDensity(chr_3, simulate = FALSE)
 ```
@@ -1443,28 +1605,30 @@ define how fine or coarse the feature grouping should be. Especially for larger
 experiments, with more samples and also larger variation in retention time it
 might not always be possible to completely separate all closely eluting ions
 from each other and sometimes it might be acceptable to group them into a single
-feature (keeping in mind that this feature would then however represent signal
-from different ions/compounds).
+feature (keeping in mind that this feature would then however potentially
+represent signal from multiple different ions/compounds).
 
 Similar to the peak detection and alignment results, also the results from the
 correspondence analysis were added to the `XcmsExperiment` object. These can be
 extracted with the `featureDefinitions` function, that extracts the *definition*
 of the LC-MS features and the `featureValues` function that extracts the
 numerical matrix with the feature abundances (in all samples). Below we extract
-the definition of the features and display the first 6 rows
+the definition of the features and display the first 6 rows.
 
 ```{r correspondence-featureDefinitions}
 #' Definition of the features
-featureDefinitions(data) |>
+featureDefinitions(mse) |>
     head()
 ```
 
 Each row defines one feature and provides information on it's *m/z* (column
-`"mzmed"`) and retention time (column `"rtmed"`). Additional columns list the
-number of chromatographic peaks that were assigned to the feature and the MS
-level. Column `"peakidx"` provides the indices of the chromatographic peaks in
-the `chromPeaks` matrix that were assigned to the feature - but generally users
-will not need or extract that information.
+`"mzmed"`) and retention time (column `"rtmed"`). The *-min* and *-max* columns
+list the minimum and maximum rt or *m/z* value of the chromatographic peaks
+assigned to the feature. Additional columns list the number of chromatographic
+peaks that were assigned to the feature and the MS level. Column `"peakidx"`
+provides the indices of the chromatographic peaks in the `chromPeaks` matrix
+that were assigned to the feature - but generally users will not need or extract
+that information.
 
 The feature abundance matrix, which is the final result of the *xcms*
 preprocessing, can be extracted with the `featureValues` function. By default,
@@ -1472,16 +1636,16 @@ with parameter `method = "maxint"`, it returns for each feature the integrated
 peak signal of the chromatographic peak with the highest signal per sample. Note
 that this has only an effect for features with more than one chromatographic
 peak per sample (i.e., if multiple chromatographic peaks in the **same** sample
-were grouped into the feature because of their closeness in retention
-time). Setting `method = "sum"` would in contrast sum the abundances of such
-chromatographic peaks. Note that `method = "sum"` is only suggested if, like in
-our example, neighboring and overlapping peaks per sample were merged to avoid
-an overestimation of the feature abundance. Below we extract the feature
+were grouped into the feature because of their closeness in retention time and
+*m/z* value). Setting `method = "sum"` would in contrast sum the abundances of
+such chromatographic peaks. Note that `method = "sum"` is only suggested if,
+like in our example, neighboring and overlapping peaks per sample were merged to
+avoid an overestimation of the feature abundance. Below we extract the feature
 abundances and show the first 6 rows.
 
 ```{r}
 #' Get abundances for the first 6 features
-featureValues(data, method = "sum") |>
+featureValues(mse, method = "sum") |>
     head()
 ```
 
@@ -1516,35 +1680,35 @@ mz_rt <- rbind(c(109.661, 109.664, 192, 200),
                c(130.959, 130.961, 197, 201))
 
 #' Extract their EICs and plot them
-chrs <- chromatogram(data, mz = mz_rt[, 1:2], rt = mz_rt[, 3:4])
+chrs <- chromatogram(mse, mz = mz_rt[, 1:2], rt = mz_rt[, 3:4])
 plot(chrs, col = c("red", "blue"), lwd = 2)
 ```
 
 Indeed, for all these feature, chromatographic peak detection failed to identify
-a peak in one of the two samples. For the features in the upper panel, the
-signal was most likely too low, while for the bottom left feature the signal was
-likely too noisy, and for the bottom right too sparse (i.e. to few data points
-to properly detect a peak). In all cases, however, signal from (presumably) the
-same ion was measured in both samples. Thus, reporting a missing value would not
-be correct for these.
-
-The aim of the *gap filling* is now to rescue signal for such features by
+a peak in one of the two samples (detected chromatographic peaks are indicated
+with a grey background color in the plot above). For the features in the upper
+panel, the signal was most likely too low, while for the bottom left feature the
+signal was likely too noisy, and for the bottom right too sparse (i.e. to few
+data points to properly detect a peak). In all cases, however, signal from
+(presumably) the same ion was measured in both samples. Thus, reporting a
+missing value would not be correct for these.
+
+The aim of the *gap filling* is now to *rescue* signal for such features by
 integrating the intensities measured within the feature's *m/z* - retention time
 area in the sample(s) in which no chromatographic peak was detected. In *xcms*
 this can be done with the `fillChromPeaks` function and the `ChromPeakAreaParam`
-which allows to configure this algorithm. Below we perform the gap filling
-showing also the number of missing values before and after running
-`fillChromPeaks`.
+to configure the gap filling. Below we perform gap filling showing also the
+number of missing values before and after running `fillChromPeaks`.
 
 ```{r fillChromPeaks}
 #' Number of missing values
-sum(is.na(featureValues(data)))
+sum(is.na(featureValues(mse)))
 
 #' Perform gap filling
-data <- fillChromPeaks(data, param = ChromPeakAreaParam())
+mse <- fillChromPeaks(mse, param = ChromPeakAreaParam())
 
 #' Number of missing values after gap filling
-sum(is.na(featureValues(data)))
+sum(is.na(featureValues(mse)))
 ```
 
 With `fillChromPeaks` we could thus *rescue* signal for all but 26
@@ -1553,7 +1717,7 @@ filled-in. Below we visualize the gap-filled chromatographic peaks for these.
 
 ```{r features-missing-values-filled, fig.cap = "Features with filled-in signal."}
 #' Extract their EICs and plot them
-chrs <- chromatogram(data, mz = mz_rt[, 1:2], rt = mz_rt[, 3:4])
+chrs <- chromatogram(mse, mz = mz_rt[, 1:2], rt = mz_rt[, 3:4])
 plot(chrs, col = c("red", "blue"), lwd = 2)
 ```
 
@@ -1562,23 +1726,27 @@ could be filled-in (i.e. which still have missing values in one of the two
 samples).
 
 ```{r gap-filling-missing, fig.cap = "Features with missing values even after gap-filling.", fig.width = 12, fig.height = 10}
-#' Identify features with missing values
-fts <- rownames(featureValues(data))[is.na(rowSums(featureValues(data)))]
+#' Identify rows (features) with at least one missing value across samples
+any_na <- featureValues(mse) |>
+    rowSums() |>
+    is.na()
+#' Get the feature IDs for these
+fts <- rownames(featureValues(mse))[any_na]
 
 #' Extract the m/z - rt regions for these features
-mz_rt <- featureArea(data, features = fts)
+mz_rt <- featureArea(mse, features = fts)
 
 #' Expand the retention time by 1 second on both sides
 mz_rt[, "rtmin"] <- mz_rt[, "rtmin"] - 1
 mz_rt[, "rtmax"] <- mz_rt[, "rtmax"] + 1
 
-chrs_na <- chromatogram(data, mz = mz_rt[, c("mzmin", "mzmax")],
+chrs_na <- chromatogram(mse, mz = mz_rt[, c("mzmin", "mzmax")],
                         rt = mz_rt[, c("rtmin", "rtmax")])
 
 plot(chrs_na, col = c("red", "blue"), lwd = 2)
 ```
 
-For these features indeed no signal was measured in the second sample.
+For these features indeed signal was measured only in one of the two samples.
 
 An alternative way to confirm if gap-filling was able to correctly rescue
 signals is to plot, for features with at least one missing value, the average
@@ -1595,10 +1763,10 @@ values are plotted against each other.
 
 ```{r comparison-detected-filled, fig.cap = "Detected (x-axis) against filled (y-axis) signal. The black solid line represents the identity line."}
 #' Get only detected signal
-vals_detect <- featureValues(data, filled = FALSE)
+vals_detect <- featureValues(mse, filled = FALSE)
 
 #' Get detected and filled-in signal
-vals_filled <- featureValues(data)
+vals_filled <- featureValues(mse)
 
 #' Replace detected signal with NA
 vals_filled[!is.na(vals_detect)] <- NA
@@ -1638,9 +1806,9 @@ l <- lm(log2(avg_filled) ~ log2(avg_detect))
 summary(l)
 ```
 
-With a value of 1.005, the slope of the line is thus very close to the slope of
+With a value of 0.994, the slope of the line is thus very close to the slope of
 the identity line and the two sets of values are also highly correlated (R
-squared of 0.81).
+squared of 0.79).
 
 
 
@@ -1656,18 +1824,18 @@ function:
 
 ```{r}
 #' Process history
-processHistory(data)
+processHistory(mse)
 ```
 
 An individual parameter object can be extracted as follows:
 
 ```{r}
 #' Peak detection parameters
-processHistory(data)[[1]]@param
+processHistory(mse)[[1]]@param
 ```
 
 Thus, the used preprocessing algorithms along with all their settings are
-reported along the preprocessing results.
+reported along with the preprocessing results.
 
 As described above, values for the individual features can be extracted from the
 result object with the `featureValues` function and the definition of the
@@ -1675,7 +1843,7 @@ features (which could be used for an initial annotation of the features based on
 their *m/z* and/or retention times) using the `featureDefinitions` function. In
 addition, the `XcmsExperiment` result object, through the internal `Spectra`
 object, keeps a *link* to the full MS data used for the analysis. For downstream
-analyses, that don't need access to this MS data anymore the preprocessing
+analyses, that don't need access to this MS data anymore, the preprocessing
 results could be represented equally well using a `SummarizedExperiment` object,
 which is Bioconductor's standard container for large-scale omics data. *xcms*
 provides with the `quantify` function a convenience function to extract all
@@ -1686,7 +1854,7 @@ results from an `XcmsExperiment` result object and return it as a
 ```{r}
 #' Extract results as a SummarizedExperiment
 library(SummarizedExperiment)
-res <- quantify(data, method = "sum")
+res <- quantify(mse, method = "sum")
 ```
 
 The sample annotations can now be accessed with the `colData` function and the
@@ -1710,7 +1878,20 @@ assay(res) |>
     head()
 ```
 
-The `SummarizedExperiment` can be subset by rows and/or columns.
+`SummarizedExperiment` objects allow also to have multiple *assay*-matrices. We
+could for example, in addition to the full feature value matrix, also add a
+second assay with only the signals from detected chromatographic peaks
+(i.e. without the gap-filled data).
+
+```{r}
+#' Assign a new assay to the SummarizedExperiment result object
+assay(res, "raw_detected") <- featureValues(mse, method = "sum",
+                                            filled = FALSE)
+```
+
+The `SummarizedExperiment` can be subset by rows and/or columns. Such subset
+operations will correctly subset row- and column data as well as all present
+assay matrices keeping the structure of the data sub set intact.
 
 ```{r}
 #' Subset to the first 10 features
@@ -1726,8 +1907,8 @@ done with a few lines of R code also more advanced (but not always needed)
 normalization algorithms are available in e.g. Bioconductor's
 `r Biocpkg("preprocessCore")` package.
 
-Differential abundance analysis could be performed using the `r
-Biocpkg("limma")` package or with any of the other packages or methods
+Differential abundance analysis could be performed using the
+`r Biocpkg("limma")` package or with any of the other packages or methods
 available in R.
 
 As mentioned above, many chromatographic peaks (and subsequently also features)
@@ -1741,13 +1922,13 @@ grouping of features through a variety of different methods. See also the
 vignette](https://sneumann.github.io/xcms/articles/LC-MS-feature-grouping.html)
 in *xcms* for more details.
 
-Finally, the `r Biocpkg("MetaboAnnotation")` provides functions to assist
-in the annotation of features from LC-MS as well as LC-MS/MS experiments. These
-allow to either perform an initial annotation based on *m/z* values or through a
-combination of *m/z* and retention time values. In addition, also annotations
-based on fragment spectra (if available) are supported (with or without
-considering in addition the features' retention times. More information is
-provided in the [MetaboAnnotation
+Finally, the `r Biocpkg("MetaboAnnotation")` package provides functions to
+assist in the annotation of features from LC-MS as well as LC-MS/MS
+experiments. These allow to either perform an initial annotation based on *m/z*
+values or through a combination of *m/z* and retention time values. In addition,
+also annotations based on fragment spectra (if available) are supported (with or
+without considering in addition the features' retention times. More information
+is provided in the [MetaboAnnotation
 vignette](https://rformassspectrometry.github.io/MetaboAnnotation/articles/MetaboAnnotation.html)
 or [MetaboAnnotation
 tutorial](https://jorainer.github.io/MetaboAnnotationTutorials/articles/annotation-use-cases.html).
@@ -1761,17 +1942,186 @@ workshops/tutorials at
 
 # Final words
 
-- Don't use default settings for preprocessing algorithms.
 - Use the infrastructure provided by the RforMassSpectrometry package ecosystem
   to inspect, explore and summarize the data.
 - Unleash the power of R!
   - Create own visualization/summarization functions if needed.
   - Combine functionalities from different packages.
   - Create customized (and reproducible) analysis workflows.
+- Don't use default settings for *xcms* preprocessing algorithms.
 
 
 # Appendix
 
+## Additional analyses performed on chromatographic or spectra data of preprocessing results
+
+While we used the basic functionality from *xcms* to perform the preprocessing
+of an LC-MS experiment, more functionality and visualization options would be
+available in the infrastructure provided through the *xcms*, *Spectra*,
+*MsCoreUtils*, *MetaboCoreUtils* and other related Bioconductor packages. It
+would for example be easily possible to extract specific information for
+selected chromatographic peaks or LC-MS features from an *xcms* result object
+and perform some additional visualizations or analyses on them. Below we first
+identify chromatographic peaks that would match the *m/z* of serine.
+
+```{r}
+#' Extract chromatographic peaks matching the m/z of the [M+H]+ of serine
+serine_pks <- chromPeaks(mse, mz = serine_mz, ppm = 20)
+serine_pks
+```
+
+The `chromPeakChromatograms` function can then be used to extract the EIC of a
+specific chromatographic peak in a sample.
+
+```{r}
+#' Extract EIC for the signal in the second sample
+serine_eic_2 <- chromPeakChromatograms(mse, peaks = rownames(serine_pks)[2])
+```
+
+We can also extract the full MS1 scan (spectrum) at the apex position of that
+chromatographic peak using the `chromPeakSpectra` function with parameters
+`msLevel = 1` and `method = "closest_rt"`.
+
+```{r}
+# Extract the full MS1 scan for the chrom peak at apex position
+serine_ms1_2 <- chromPeakSpectra(mse, msLevel = 1, method = "closest_rt",
+                                 peaks = rownames(serine_pks)[2])
+```
+
+For LC-MS/MS data, this function would allow to select all MS2 spectra from the
+data set with their precursor m/z (and retention time) within the
+chromatographic peak's *m/z* and retention time width using parameters `msLevel
+= 2` and `method = "all"`.
+
+Below we plot the EIC and the MS1 scan for the selected chromatographic peak.
+
+```{r, fig.cap = "EIC for the [M+H]+ ion of serine in one sample and full scan MS1 spectrum at the EIC's apex position."}
+par(mfrow = c(1, 2))
+
+#' Plot the EIC
+plot(serine_eic_2)
+#' Indicate the retention time of the full scan MS1 spectrum
+abline(v = rtime(serine_ms1_2), lty = 2)
+#' Plot the full scan MS1 spectrum
+plotSpectra(serine_ms1_2)
+#' Indicate the theoretical m/z of the
+points(serine_mz, y = -300, pch = 2, col = "red")
+```
+
+Similar information can also be extracted for LC-MS features using the
+`featureChromatograms` and `featureSpectra` functions but these functions will
+return chromatograms and spectra for **all** samples in the experiment (not just
+for a single sample). Also, importantly, while the `chromPeakChromatogram`
+extracts the EIC specific for the selected sample, i.e. using the exact *m/z*
+and retention time ranges of the chromatographic peak in that sample,
+`featureChrommatograms` will instead integrate the signal from the *m/z* and
+retention time area of the **feature**, i.e. will use a single area and
+integrate the signal from that same area in each sample. This *m/z* - retention
+time area might eventually be larger than the respective ranges for a single
+chromatographic peak in one sample. This *m/z* - retention time area for
+features can be extracted using the `featureArea` function:
+
+```{r}
+#' Extract the m/z - retention time area for features
+featureArea(mse) |>
+    head()
+```
+
+Additional analyses could now be performed on the full scan MS1 spectrum
+containing a mass peak for the [M+H]+ ion of serine. One possibility would be to
+identify potential isotope peaks of the monoisotopic mass peak of serine. The
+`isotopologies` function from the `r Biocpkg("MetaboCoreUtils")` allows for
+example to identify and group potential isotope peaks in MS1 spectrum data
+without any assumptions on the chemical formula of the compound of interest.
+
+```{r}
+#' Extract the mass peak data from the MS1 spectrum containing
+#' the signal from an ion of serine
+pd <- peaksData(serine_ms1_2)[[1]]
+head(pd)
+
+#' Identify mass peaks that could represent isotopes of the mass peak
+#' of the [M+H]+ ion of serine
+iso_idx <- isotopologues(pd, seedMz = serine_mz)
+iso_idx
+```
+
+The function thus identified two peaks that, based on their *m/z* values and
+differences in intensity, could represent isotopologues. We below highlight
+these mass peaks in the full MS1 spectrum.
+
+```{r, fig.cap = "Full scan spectrum at apex position of the signal for the [M+H]+ ion for serine. Isotopologues are indicated in red."}
+#' Set the color for each mass peak to a transparent black color
+cols <- rep("#00000040", lengths(serine_ms1_2)[1])
+#' Use a red color for the identified isotopologues
+cols[iso_idx[[1]]] <- "#ff0000ff"
+#' Plot the data
+plotSpectra(serine_ms1_2, col = cols, lwd = 2)
+```
+
+While in the example above were specifically looking for potential isotopes of a
+single, selected, mass peak (by setting `seedMz` to the *m/z* value of that
+peak), we could also use `isotopologues` to identify all potential isotope
+groups in a spectrum.
+
+```{r}
+#' Identify all potential isotope peaks in the MS1 spectrum
+iso_idx <- isotopologues(pd)
+iso_idx
+```
+
+We can also highlight these in the spectrum plot.
+
+```{r, fig.cap = "MS1 spectrum with potential isotope groups highlighted in different colors."}
+#' Define a color for all mass peaks and set it to transparent black
+cols <- rep("#00000040", lengths(serine_ms1_2)[1])
+#' Use a different color for each isotope group
+iso_cols <- rainbow(length(iso_idx))
+for (i in seq_along(iso_idx))
+    cols[iso_idx[[i]]] <- iso_cols[i]
+plotSpectra(serine_ms1_2, col = cols, lwd = 2)
+```
+
+Note that such isotopologue identification is not limited to data from a MS1
+spectrum. We could also identify features representing signal from
+potential isotopes. For this we below create a `matrix` with the features' *m/z*
+values and the maximum intensity of the feature in one of the samples and apply
+the `isotopologues` function on it.
+
+```{r}
+#' Define a matrix with the m/z and intensity values for each feature.
+#' As intensity we use the highest measured intensity in the first
+#' sample
+pd <- cbind(
+    mz = featureDefinitions(mse)$mzmed,
+    intensity = featureValues(mse, value = "maxo")[, 1]
+)
+#' Ensure the matrix to be sorted by m/z value
+pd <- pd[order(pd[, "mz"]), ]
+
+#' Identify potential isotope groups
+iso_idx <- isotopologues(pd)
+
+head(iso_idx)
+```
+
+We thus identified potential isotopologues, but, because we ignored the
+retention times of the features in this simple approach, the list will contain
+also many false positives. The features of the isotope group below for example
+all have different retention times and can thus not represent signal from
+isotopes of the same compound.
+
+```{r}
+#' Show a false positive finding
+featureDefinitions(mse)[iso_idx[[3]], ]
+```
+
+Others however could be real isotopes:
+
+```{r}
+featureDefinitions(mse)[iso_idx[[2]], ]
+```
+
 
 ## Additional visualizations
 
@@ -1785,12 +2135,12 @@ space from an LC-MS experiment.
 
 We below subset the data to the first sample and visualize the identified
 chromatographic peaks in the *m/z* - retention time plane using the
-`plotChromPeaks` data function already used before.
+`plotChromPeaks` function already used before.
 
 ```{r, fig.cap = "Position of identified chromatographic peaks in the first sample."}
 #' Plot identified chromatographic peaks in the first sample
-data_1 <- data[1]
-plotChromPeaks(data_1)
+mse_1 <- mse[1]
+plotChromPeaks(mse_1)
 
 ```
 
@@ -1804,7 +2154,7 @@ the region from which the data will be extracted by 4 seconds on either side.
 
 ```{r}
 #' Extract EICs for each identified chromatographic peak
-chrs_all <- chromPeakChromatograms(data_1, expandRt = 4)
+chrs_all <- chromPeakChromatograms(mse_1, expandRt = 4)
 ```
 
 While we could now simply proceed and plot each of the `r length(chrs_all)` EICs
@@ -1826,7 +2176,7 @@ plotChromatogramsOverlay(chrs_all, stacked = 1, bty = "n",
 ```
 
 This plot thus provides a general overview of the detected signal
-(chromatographic peaks) of a data file. we can immediately spot some very high
+(chromatographic peaks) of a data file. We can immediately spot some very high
 intensity peaks, regions with higher number of ions and also signal from
 potential contaminants.
 
@@ -1836,7 +2186,7 @@ from that for a closer inspection of the data. Below we define an area from a
 
 ```{r, fig.cap = "Selected region from which EICs should be extracted and plotted."}
 #' Plot location of all peaks and highlight region of interest
-plotChromPeaks(data_1)
+plotChromPeaks(mse_1)
 rect(125, 113, 145, 119, lty = 3, border = "#ff000080", lwd = 2)
 ```
 
@@ -1846,11 +2196,11 @@ ranges and the selected (fixed) retention time range.
 
 ```{r}
 #' Extract chrom peaks from that region
-pks <- chromPeaks(data_1, mz = c(113, 119), rt = c(125, 145))
+pks <- chromPeaks(mse_1, mz = c(113, 119), rt = c(125, 145))
 
 #' Extract EICs for the m/z slices of the chromatographic peaks
 #' and the full retention time window of the area
-chrs_sub <- chromatogram(data_1, mz = pks[, c("mzmin", "mzmax")],
+chrs_sub <- chromatogram(mse_1, mz = pks[, c("mzmin", "mzmax")],
                          rt = cbind(rep(125, nrow(pks)),
                                     rep(145, nrow(pks))))
 
@@ -1878,16 +2228,15 @@ text(x = rep(126, length(mzs)), y = y[[1]],
 ```
 
 Some of the EICs seem to represent signals from isotopes (e.g. the EIC at 114.07
-and 115.07). In fact, we can use the `isotopologues` function from the `r
-Biocpkg("MetaboCoreUtils")` to check whether pairs of *m/z* and intensity values
-would match signal expected for isotopes.
+and 115.07). In fact, we can again use the `isotopologues` function from the
+`r Biocpkg("MetaboCoreUtils")` to check whether pairs of *m/z* and intensity
+values would match signal expected for isotopes.
 
 ```{r}
 #' Order the extracted chromatographic peaks by m/z
 pks <- pks[order(pks[, "mz"]), ]
 
 #' Test which signals could come from isotopes
-library(MetaboCoreUtils)
 isotopologues(pks[, c("mz", "into")])
 ```
 

From 86c2b6ea1dd5fa24dc84e0e4bb6178513c90b860 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Fri, 16 Feb 2024 08:11:53 +0100
Subject: [PATCH 30/42] Small refactoring

---
 vignettes/xcms-preprocessing.Rmd | 196 +++++++++++++++++--------------
 1 file changed, 106 insertions(+), 90 deletions(-)

diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index cff07d6..caf57fa 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -34,8 +34,8 @@ opts_chunk$set(message = FALSE, error = FALSE, warning = FALSE,
 # Abstract
 
 In this document we discuss liquid chromatography (LC) mass spectrometry (MS)
-data handling and exploration using the `r Biocpkg("MsExperiment")` and `r
-Biocpkg("Spectra")` Bioconductor packages and perform the preprocessing of a
+data handling and exploration using the `r Biocpkg("MsExperiment")` and
+`r Biocpkg("Spectra")` Bioconductor packages and perform the preprocessing of a
 small LC-MS data set using the `r Biocpkg("xcms")` package. Functionality from
 the `r Biocpkg("MetaboCoreUtils")` and `r Biocpkg("MsCoreUtils")` packages are
 used for general tasks frequently performed during metabolomics data
@@ -79,7 +79,7 @@ ratio *m/z*. The measured signal is represented as a spectrum: intensities along
 
 Many ions will result, when measured with MS alone, in a very similar
 *m/z*. Thus, making it difficult or impossible to discriminate them. MS is
-therefore frequently coupled with a second technology to separate them prior
+therefore frequently coupled with a second technology to separate ions prior
 quantification based on properties other than their mass (e.g. based on their
 polarity). Common choices are gas chromatography (GC) or liquid chromatography
 (LC). In a typical LC-MS setup a sample gets injected into the system, the
@@ -98,6 +98,16 @@ supposed to represent the signal from ions of a certain type of molecule) are
 quantified.
 
 
+## Goals of this workshop
+
+- Learn how R/*xcms* and the packages from the [RforMassSpectrometry
+  initiative](https://rformassspectrometry.org) can be used to inspect, evaluate
+  and analyze LC-MS data.
+
+- Learn the basis to build reproducible analysis workflows, tailored and
+  customized for individual data sets.
+
+
 ## Definitions and common naming convention
 
 Naming conventions and terms used in this document are:
@@ -108,24 +118,15 @@ Naming conventions and terms used in this document are:
 - *chromatographic peak detection*: process in which chromatographic peaks are
   identified within a sample (file).
 - *alignment*: process that adjusts for retention time differences
-  (i.e. possible signal drifts from the LC) between measurements/files.
+  (i.e. possible signal drifts from the LC) between measurements/samples.
 - *correspondence*: grouping of chromatographic peaks (presumably from the same
-  ion) across samples/files.
+  ion) across samples/files into *LC-MS features*.
 - *feature* (or *LC-MS features*): entity representing signal from the same type
   of ion/molecule, characterized by its specific retention time and *m/z*. In
   *xcms*, features represent identified chromatographic peaks grouped across
   samples/files.
 
 
-## Goals of this workshop
-
-- Learn how R/*xcms* and the packages from the RforMassSpectrometry initiative
-  can be used to inspect, evaluate and analyze LC-MS data.
-
-- Learn the basis to build reproducible analysis workflows, tailored and
-  customized for individual data sets.
-
-
 # Data import and exploration
 
 The example data set of this workflow consists of two files in mzML format with
@@ -181,6 +182,7 @@ The `length` of an `MsExperiment` is defined by the number of samples (files)
 within the object.
 
 ```{r general-access}
+#' Number of samples
 length(mse)
 
 ```
@@ -212,14 +214,13 @@ spectra(mse)
 
 ```
 
-From version 4 on, *xcms* uses the more modern and flexible infrastructure for
-MS data analysis provided by the `r Biocpkg("Spectra")` package. While it is
-still possible and supported to use *xcms* together with the
-`r Biocpkg("MSnbase")` package, users are advised to switch to this new
-infrastructure as it provides more flexibility and a higher performance. Also,
-through additional packages such as the `r Biocpkg("MsBackendRawFileReader")`,
-the new infrastructure would allow to import MS data also from other files
-than mzML, mzXML or CDF files.
+From version 4 on, *xcms* supports the more modern and flexible infrastructure
+for MS data analysis provided by the `r Biocpkg("Spectra")` package. While it is
+still possible to use *xcms* together with the `r Biocpkg("MSnbase")` package,
+users are advised to switch to the newer infrastructure as it provides more
+flexibility and a higher performance. Also, through additional packages such as
+the `r Biocpkg("MsBackendRawFileReader")`, the new infrastructure would allow to
+import MS data also from other files than mzML, mzXML or CDF files.
 
 In the next few examples we briefly explain the `Spectra` object and illustrate
 the use of such objects using some simple examples. More information on
@@ -242,11 +243,11 @@ spectra(mse) |>
 The `Spectra` object itself is agnostic of any sample information, it simply
 contains all spectra from the experiment, first all spectra from the first file,
 followed by the spectra from the second. The mapping of spectra to samples is
-provided by the `MsExperiment` object. To access spectra from a specific sample
+defined in the `MsExperiment` object. To access spectra from a specific sample
 we either subset the `MsExperiment` to that particular sample (as done in the
 example above) or we use the `fromFile` function that returns for each spectrum
 the index of the file within the `MsExperiment` to which it belongs. Below we
-use that function to determine the total number of spectra for each sample.
+use `fromFile` to determine the total number of spectra per sample.
 
 ```{r}
 #' Get the number of spectra per file.
@@ -255,7 +256,7 @@ fromFile(mse) |>
 ```
 
 Such basic data summaries can be helpful for a first initial quality assessment
-to potentially identify problematic data files with e.g. a unexpected low number
+to identify potentially problematic data files with e.g. a unexpected low number
 of spectra.
 
 Besides the peak data (*m/z* and intensity values) also additional spectra
@@ -271,8 +272,8 @@ spectra(mse) |>
 Thus, for all spectra we have general information such as the MS level
 (`"msLevel"`) or the retention time (`"rtime"`) available. For most of these
 spectra variables dedicated accessor functions are available (such as `msLevel`,
-`rtime`) and in addition it is possible to access any variable using `$` and the
-name of the variable (like accessing the columns of a `data.frame`). As an
+`rtime`). In addition it is possible to access any variable using `$` and the
+name of the variable (similar to accessing the columns of a `data.frame`). As an
 example we extract below the `msLevel` spectra variable and use the `table`
 function on the result to get an overview of the number of spectra from
 different MS levels available in the object.
@@ -327,7 +328,8 @@ intensity(sp)
 ```
 
 As an alternative, the `peaksData` function could be used to extract both the
-*m/z* and intensity (as numeric matrices) with a single function call.
+*m/z* and intensity values (as two-column numeric matrix) with a single function
+call.
 
 The total ion signal of a spectrum could be calculated by simply summing the
 intensities of all peaks in the spectrum. Below we perform that operation on the
@@ -345,7 +347,8 @@ determine the distribution of these using the `quantile` function.
 
 ```{r}
 #' Calculate the distribution of total ion signal of the first file
-spectra(mse[1]) |>
+mse[1] |>
+    spectra() |>
     intensity() |>
     sum() |>
     quantile()
@@ -355,7 +358,8 @@ We repeat the operation for the second file.
 
 ```{r}
 #' Repeat for the second file
-spectra(mse[2]) |>
+mse[2] |>
+    spectra() |>
     intensity() |>
     sum() |>
     quantile()
@@ -385,7 +389,7 @@ similarly to the total ion intensity in the previous section, we use below the
 data (e.g. from an `MsExperiment` object). With parameter `aggregationFun =
 "max"` we define to report the maximum signal per spectrum (setting
 `aggregationFun = "sum"` would in contrast sum up all intensities of a spectrum
-and hence create a TIC).
+and hence return a TIC).
 
 ```{r}
 #' Extract and plot a BPC
@@ -401,13 +405,13 @@ eluted. Also, the BPC of the two data files look similar, which is expected
 since both represent the same sample.
 
 In addition to a visual inspection it is, especially for larger data sets,
-important to also *quantitatively* compare the data and hence derive quality
-metrics of a data set. For our base peak signals, however, retention times will
-be slightly different between the samples preventing thus a direct comparison
-and evaluation of the data. An easy solution to this is to *bin* the data into
-equal sized bins along the retention time axis and aggregate the measured
-intensities within each bin (per sample). Below we bin the data with a bin size
-of 1 second returning for each bin the maximal signal.
+important to also *quantitatively* compare the data and derive quality metrics
+of a data set. For our base peak signals, however, retention times will be
+slightly different between the samples preventing thus a direct comparison and
+evaluation of this data. An easy solution to this is to *bin* the data along the
+retention time axis into equal sized bins and aggregate the measured intensities
+within each bin (per sample). Below we bin the data with a bin size of 1 second
+reporting the maximal signal per bin.
 
 ```{r}
 #' Bin the BPC
@@ -415,8 +419,8 @@ bpc_bin <- bin(bpc, binSize = 1)
 ```
 
 After binning, the two chromatograms have the same retention times (and number
-of intensities) and we can e.g. combine the intensity vectors into a matrix of
-intensities.
+of intensities) and we can thus *bind* their intensity vectors as columns of a
+new numeric matrix using `cbind`:
 
 ```{r}
 #' Create an intensity matrix
@@ -433,28 +437,31 @@ LC-MS runs.
 cor(bpc_mat[, 1], bpc_mat[, 2])
 ```
 
-For more than two samples a correlation matrix could be calculated:
+We could also create a correlation matrix with the pairwise correlation
+coefficients of all samples against all samples. This would be particularly
+helpful for data sets with more than two samples.
 
 ```{r}
 #' Create a pairwise correlation matrix
 cor(bpc_mat)
 ```
 
-And such a correlation matrix could also be easily visualized as a *heatmap* -
+Such a correlation matrix could also be easily visualized as a *heatmap* -
 with the additional possibility to cluster samples with similar BPC. While for
 the present, two-sample data set, this is not very informative, for larger data
 sets it can help to evaluate differences between batches or to spot outlier
-samples (or rather LC-MS measurement runs).
+samples (or rather outlier LC-MS measurement runs).
 
 ```{r, fig.cap = "Heatmap for similarity of the BPC of the two data files"}
 #' Create a heatmap of the correlation matrix
+library(pheatmap)
 cor(bpc_mat) |>
     pheatmap()
 ```
 
 This also exemplifies the power of an R-based analysis workflow that allows us
-to combine LC-MS specific analysis methods provided by the *xcms* package with
-build-in R functions or (statistical) data analysis methods provided by any
+to combine LC-MS specific analysis methods provided by e.g. the *xcms* package
+with build-in R functions or (statistical) data analysis methods provided by any
 other R package.
 
 The BPC collapsed the 3-dimensional LC-MS data (*m/z* by retention time by
@@ -488,9 +495,8 @@ spectra from one file (by using `f = fromFile(mse)`) into a single spectrum
 containing mass peaks present in any of the spectra of that file. Mass peaks
 with a difference in their *m/z* that is smaller than `ppm` (parts-per-million
 of the *m/z* value) are combined into one peak for which the maximal intensity
-of the grouped peaks is reported. Note that we set `ppm` to a rather small value
-because the present data is in profile mode. In general, it is suggested to use
-a rather small value for `ppm` for `combineSpectra` on MS1 data.
+of the grouped peaks is reported. Note that it is suggested to use a small value
+for `ppm` to combine MS1 spectra with `combineSpectra`.
 
 ```{r}
 #' Combine all spectra of one file into a single spectrum
@@ -521,19 +527,19 @@ do.call(cbind, intensity(bps_bin)) |>
     cor()
 ```
 
-Alternatively, we can also calculate the similarity between the base peak
-spectra using the `compareSpectra` function and one of the available peak
-similarity measures. Below we use the normalized dot product to calculate
-the similarity between the two spectra matching peaks using an *m/z* tolerance
-of 20 ppm.
+Alternatively, we can also directly calculate the similarity between the base
+peak spectra using the `compareSpectra` function and one of the available peak
+similarity measures. Below we use the normalized dot product to calculate the
+similarity between the two spectra matching peaks using an *m/z* tolerance of 10
+ppm.
 
 ```{r}
 #' Calculate normalized dot product similarity between the spectra
-compareSpectra(bps, ppm = 20, FUN = MsCoreUtils::ndotproduct)
+compareSpectra(bps, ppm = 10, FUN = MsCoreUtils::ndotproduct)
 ```
 
 These measures thus allow us to get some general information on a data set and
-evaluate similarities between the samples.
+evaluate similarities between the samples of an experiment.
 
 
 ### Detailed data inspection
@@ -563,6 +569,7 @@ from which the data was imported (see example below) and use that to determine
 the originating sample, but that would involve additional R code.
 
 ```{r}
+#' List the original data file for each spectrum
 basename(dataOrigin(sps))
 ```
 
@@ -572,8 +579,8 @@ function. This filters the `Spectra` object *within* the `MsExperiment`
 retaining all associations (links) between samples and subset spectra. While
 some of the most commonly used filter functions, such as `filterRt` or
 `filterMsLevel`, are also implemented for `MsExperiment`, the `filterSpectra`
-function allows to apply any of the filter functions available for `Spectra`
-objects to the data.
+function allows to apply any of the many filter functions available for
+`Spectra` objects to the data.
 
 ```{r}
 #' Subset the whole MsExperiment
@@ -640,7 +647,7 @@ function. Note that alternatively we could also first filter the data set by
 
 ```{r, fig.cap = "Ion trace for an ion of serine"}
 #' Extract a full RT chromatogram for ions with an m/z similar than serine
-serine_chr <- chromatogram(mse, mz = serine_mz + c(-0.01, 0.01))
+serine_chr <- chromatogram(mse, mz = serine_mz + c(-0.005, 0.005))
 plot(serine_chr)
 ```
 
@@ -689,7 +696,7 @@ extracted ion chromatogram (EIC, sometimes also referred to as XIC) for the
 #' Create an EIC for serine
 mse |>
     filterRt(rt = c(175, 189)) |>
-    filterMzRange(mz = serine_mz + c(-0.01, 0.01)) |>
+    filterMzRange(mz = serine_mz + c(-0.005, 0.005)) |>
     chromatogram() |>
     plot()
 ```
@@ -716,7 +723,7 @@ peak (the centroid) is called centroiding. This process results in much smaller
 file sizes, with only little information loss. *xcms*, specifically the
 *centWave* chromatographic peak detection algorithm, was designed for centroided
 data, thus, prior to data analysis, profile data, such as the example data used
-here, should be centroided.
+here, has to be centroided.
 
 Below we inspect the profile data for one of the spectra extracted above and
 focus on the mass peak for serine.
@@ -754,7 +761,7 @@ each point represents one mass peak with its intensity being color coded (blue
 representing low, yellow high intensity). Each column of data points represents
 data from the same spectrum. The upper panel of the plot shows a chromatographic
 visualization of the data from the lower panel, i.e., for each retention time
-(spectrum) the sum of intensities is shown.
+(spectrum) the sum of intensities within the *m/z* range is shown.
 
 Note that, while it would be possible to create such a plot for the full MS data
 of an experiment, this type of visualization works best for small *m/z* -
@@ -1278,17 +1285,19 @@ across samples. Below we use the *peakDensity* method for correspondence
 (details about this method and explanations on the choices of its parameters are
 provided in the next section). In brief, parameter `sampleGroups` defines to
 which sample group of the experiment individual samples belong to, and parameter
-`minFraction` specifies the proportion of samples (of one sample group) in which
-a chromatographic peak needs to be identified (for a particular *m/z* -
-retention time region) to group them into an LC-MS feature. For our example we
-use the sample group definition in the `sampleData` of our `mse` variable and
-set `minFraction = 1` requiring thus a chromatographic peak to be identified in
-all (100%) of available samples to define a feature. Generally, if
-correspondence is performed on more heterogeneous samples, `minFraction` values
-between 0.6 and 0.8 could be used instead. Since the aim of this initial
-correspondence is to define some (presumably well separated) groups of
-chromatographic peaks across the samples, its settings does not need to be fully
-optimized.
+`minFraction` specifies the proportion of samples (of one of the sample groups
+defined in `sampleGroups`) in which a chromatographic peak needs to be detected
+to group them into an LC-MS feature. Chromatographic peaks will be grouped into
+features if their difference in *m/z* and retention times is below the defined
+thresholds and if in at least `minFraction * 100` percent of samples of at least
+one sample group a chromatographic peak was detected. For our example we use the
+sample group definition in the `sampleData` of our `mse` variable and set
+`minFraction = 1` requiring thus a chromatographic peak to be identified in all
+(100%) of available samples to define a feature. Generally, if correspondence is
+performed on more heterogeneous samples, `minFraction` values between 0.6 and
+0.8 could be used instead. Since the aim of this initial correspondence is to
+define some (presumably well separated) groups of chromatographic peaks across
+the samples, its settings does not need to be fully optimized.
 
 ```{r}
 #' Define the settings for the initial peak grouping - details for
@@ -1300,18 +1309,20 @@ mse <- groupChromPeaks(mse, pdp)
 
 This step now grouped chromatographic peaks across samples and defined so called
 LC-MS features (or simply features). We can thus now run the alignment using the
-*peakGroups* algorithm. The main parameter to define the anchor peaks is
-`minFraction`. Similar to the definition above, `minFraction` refers to the
-proportion of samples in which a chromatographic peak needs to be present. By
+*peakGroups* algorithm that aligns the data by minimizing differences in
+retention times of *anchor peaks* (i.e. selected features with chromatographic
+peaks detected in most samples). The main parameter to define these anchor peaks
+is `minFraction`. Similar to the definition above, `minFraction` refers to the
+proportion of samples in which a chromatographic peak needs to be present, only,
+here we don't consider the different sample groups, but the whole data set. By
 setting `minFraction = 1` we base the alignment on features with peaks
-identified in 100% of the samples in the data set. For alignments that are
-based on repeatedly measured samples (e.g. also for subset-based alignment on
-sample pools) values `>= 0.9` can be used. Otherwise, values between 0.7 and 0.9
-might be more advisable to ensure that a reasonable set of features are
-selected.
+identified in 100% of the samples in the data set. For alignments that are based
+on repeatedly measured samples (e.g. also for subset-based alignment on sample
+pools) values `>= 0.9` can be used. Otherwise, values between 0.7 and 0.9 might
+be more advisable to ensure that a reasonable set of features are selected.
 
-To evaluate anchor peaks that would be selected based the defined settings, we
-can use the `adjustRtimePeakGroups` method:
+To evaluate anchor peaks that would be selected based on the defined settings,
+we can also use the `adjustRtimePeakGroups` method:
 
 ```{r}
 #' Get the anchor peaks that would be selected
@@ -1374,7 +1385,6 @@ after alignment. In a similar way as before, we set `chromPeaks = "none"` in the
 `chromatogram` call to tell the function to **not** include any identified
 chromatographic peaks in the returned chromatographic data.
 
-
 ```{r bpc-raw-adjusted, fig.cap = "BPC before (top) and after (bottom) alignment."}
 par(mfrow = c(2, 1))
 
@@ -1419,7 +1429,6 @@ spectra. The adjusted retention times are stored as a new spectra variable
 result object will by default return these (adjusted) values.
 
 
-
 ## Correspondence
 
 The final step of the LC-MS preprocessing with *xcms* is the correspondence
@@ -1567,8 +1576,14 @@ mse
 
 The present data set is restricted to a quite narrow *m/z* range, thus, the
 parameter `ppm` does not have a strong impact. For *real* data sets, this
-parameter results in an *m/z*-dependent *m/z* width of detected features. See
-also the [*xcms vignette*](LLLLL) for an example.
+parameter results in an *m/z*-dependent *m/z* width of detected features. For
+`binSize = 0.01` and `ppm = 10` and a data set with an *m/z* range from 0 to
+1000, the width of the *m/z* bins would linearly increase, along the *m/z* axis,
+from an initial size of 0.01 up to a value of 0.02 (the static size of 0.01 plus
+10 ppm of the maximal *m/z* of 1000 which results in the maximal bin size of
+0.02). See also [*this github
+issue*](https://github.com/sneumann/xcms/issues/711#issuecomment-1893389759) for
+an example and discussion.
 
 Over 300 features were identified in our example data set. Again, it is
 suggested to evaluate the results on selected compounds/ions. We therefore
@@ -1580,7 +1595,7 @@ correspondence analysis (based also on the settings that were used) by setting
 
 ```{r correspondence-evaluate, fig.cap = "Result of correspondence on an *m/z* slice containing the isomers valine and betaine."}
 #' Extract chromatogram including signal for betaine and valine
-chr_2 <- chromatogram(mse, mz = 118.08625 + c(-0.01, 0.01),
+chr_2 <- chromatogram(mse, mz = 118.08625 + c(-0.005, 0.005),
                       aggregationFun = "max")
 #' Setting simulate = FALSE to show the actual correspondence results
 plotChromPeakDensity(chr_2, simulate = FALSE)
@@ -1591,7 +1606,7 @@ from isomers leucine and isoleucine ([M+H]+ *m/z* 132.10191).
 
 ```{r correspondence-evaluate-2, fig.cap = "Result of correspondence on an *m/z* slice containing the isomers leucine and isoleucine."}
 #' Extract chromatogram with signal for isomers leucine and isoleucine
-chr_3 <- chromatogram(mse, mz = 132.10191 + c(-0.01, 0.01),
+chr_3 <- chromatogram(mse, mz = 132.10191 + c(-0.005, 0.005),
                       aggregationFun = "max")
 plotChromPeakDensity(chr_3, simulate = FALSE)
 ```
@@ -1851,7 +1866,7 @@ results from an `XcmsExperiment` result object and return it as a
 `SummarizedExperiment`. This function takes the same parameters than the
 `featureValues`, which is internally used to extract the feature value matrix.
 
-```{r}
+```{r, warning = FALSE, message = FALSE}
 #' Extract results as a SummarizedExperiment
 library(SummarizedExperiment)
 res <- quantify(mse, method = "sum")
@@ -1942,6 +1957,7 @@ workshops/tutorials at
 
 # Final words
 
+- `xcms` is not a single, monolithic software, but part of a package ecosystem.
 - Use the infrastructure provided by the RforMassSpectrometry package ecosystem
   to inspect, explore and summarize the data.
 - Unleash the power of R!
@@ -2030,7 +2046,7 @@ featureArea(mse) |>
 Additional analyses could now be performed on the full scan MS1 spectrum
 containing a mass peak for the [M+H]+ ion of serine. One possibility would be to
 identify potential isotope peaks of the monoisotopic mass peak of serine. The
-`isotopologies` function from the `r Biocpkg("MetaboCoreUtils")` allows for
+`isotopologues` function from the `r Biocpkg("MetaboCoreUtils")` allows for
 example to identify and group potential isotope peaks in MS1 spectrum data
 without any assumptions on the chemical formula of the compound of interest.
 

From e0d5e6d021828cee52fcc0d62d807a4cf81b92fe Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Fri, 16 Feb 2024 15:16:07 +0100
Subject: [PATCH 31/42] Small fixes and updates

---
 vignettes/xcms-preprocessing.Rmd | 99 ++++++++++++++++----------------
 1 file changed, 50 insertions(+), 49 deletions(-)

diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index caf57fa..f365c02 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -893,7 +893,7 @@ EIC for serine and run a *centWave*-based peak detection on that data using
 ```{r centWave-default}
 #' Get the EIC for serine in all files
 serine_chr <- chromatogram(mse, rt = c(164, 200),
-                           mz = serine_mz + c(-0.01, 0.01),
+                           mz = serine_mz + c(-0.005, 0.005),
                            aggregationFun = "max")
 
 #' Get default centWave parameters
@@ -906,7 +906,7 @@ chromPeaks(res)
 
 The peak matrix returned by `chromPeaks` is empty, thus, with the default
 settings *centWave* failed to identify any chromatographic peak in the EIC for
-serine. These default values are shown below:
+serine. The default values for the parameters are shown below:
 
 ```{r centWave-default-parameters}
 #' Default centWave parameters
@@ -920,6 +920,7 @@ however see that these values are way too large for our UHPLC-based data set
 (see below).
 
 ```{r, fig.cap = "Extracted ion chromatogram for serine."}
+#' Plot the EIC
 plot(serine_chr)
 ```
 
@@ -1273,21 +1274,21 @@ repeatedly measured QC samples (e.g. sample pools) and adjust the full
 experiment based on these. See the alignment section in the *xcms*
 [vignette](https://bioconductor.org/packages/release/bioc/vignettes/xcms/inst/doc/xcms.html)
 for more information on this subset-based alignment. Note that such a
-subset-based alignment requires the samples to be loaded in the order in which
-they were measured. Also, recently, functionality was added to *xcms* to perform
-the alignment on pre-selected signals (e.g. retention times of internal
+subset-based alignment requires the samples to be organized in the order in
+which they were measured. Also, recently, functionality was added to *xcms* to
+perform the alignment on pre-selected signals (e.g. retention times of internal
 standards) or to align a data set against an external reference.
 
 For our example we use the *peakGroups* method that, as mentioned above, aligns
 samples based on the retention times of *anchor peaks*. To define these, we need
-to first run an initial correspondence analysis to group chromatographic peaks
+to first run an initial correspondence analysis and group chromatographic peaks
 across samples. Below we use the *peakDensity* method for correspondence
 (details about this method and explanations on the choices of its parameters are
 provided in the next section). In brief, parameter `sampleGroups` defines to
 which sample group of the experiment individual samples belong to, and parameter
 `minFraction` specifies the proportion of samples (of one of the sample groups
 defined in `sampleGroups`) in which a chromatographic peak needs to be detected
-to group them into an LC-MS feature. Chromatographic peaks will be grouped into
+to group them into an LC-MS feature. Chromatographic peaks will be grouped to
 features if their difference in *m/z* and retention times is below the defined
 thresholds and if in at least `minFraction * 100` percent of samples of at least
 one sample group a chromatographic peak was detected. For our example we use the
@@ -1303,7 +1304,7 @@ the samples, its settings does not need to be fully optimized.
 #' Define the settings for the initial peak grouping - details for
 #' choices in the next section.
 pdp <- PeakDensityParam(sampleGroups = sampleData(mse)$group, bw = 1.8,
-                        minFraction = 1, binSize = 0.02, ppm = 10)
+                        minFraction = 1, binSize = 0.01, ppm = 10)
 mse <- groupChromPeaks(mse, pdp)
 ```
 
@@ -1330,9 +1331,9 @@ pgm <- adjustRtimePeakGroups(mse, PeakGroupsParam(minFraction = 1))
 head(pgm)
 ```
 
-Ideally, if possible, the anchor peaks should span a large range of the
-retention time range to allow alignment of the full LC runs. Below evaluate the
-distribution of retention times of the anchor peaks in the first sample.
+Ideally, if possible, the anchor peaks should span most of the retention time
+range to allow alignment of the full LC runs. Below evaluate the distribution of
+retention times of the anchor peaks in the first sample.
 
 ```{r}
 #' Evaluate distribution of anchor peaks' rt in the first sample
@@ -1346,9 +1347,9 @@ on the `minFraction` parameter) the algorithm minimizes the observed
 between-sample retention time differences for these. Parameter `span` defines
 the degree of smoothing of the loess function that is used to allow different
 regions along the retention time axis to be adjusted by a different factor. A
-value of 0 will most likely cause overfitting, while 1 would cause all retention
-times of a sample to be shifted by a constant value. Values between 0.4 and 0.6
-seem to be reasonable for most experiments.
+value close to 0 will most likely cause overfitting, while a value of 1 would
+cause all retention times of a sample to be shifted by a constant value. Values
+between 0.4 and 0.6 seem to be reasonable for most experiments.
 
 ```{r alignment-correspondence}
 #' Define settings for the alignment
@@ -1474,10 +1475,10 @@ assignment defined in `sampleData`.
 
 ```{r}
 #' Extract a chromatogram for a m/z range containing serine
-chr_1 <- chromatogram(data, mz = serine_mz + c(-0.005, 0.005))
+chr_1 <- chromatogram(mse, mz = serine_mz + c(-0.005, 0.005))
 
 #' Default parameters for peak density; bw = 30
-pdp <- PeakDensityParam(sampleGroups = sampleData(data)$group, bw = 30)
+pdp <- PeakDensityParam(sampleGroups = sampleData(mse)$group, bw = 30)
 
 #' Test these settings on the extracted slice
 plotChromPeakDensity(chr_1, param = pdp)
@@ -1497,22 +1498,22 @@ of this curve (which is created with the base R `density` function) is
 configured with the parameter `bw`. The *peakDensity* algorithm assigns all
 chromatographic peaks within the same *peak* of this density estimation curve to
 the same feature. Chromatographic peaks assigned to the same feature are
-indicated with a grey rectangle in the plot. In the present example, because
-retention times of the two chromatographic peaks are very similar, this
-rectangle is very narrow and looks thus more like a vertical line. Based on this
-result, the default settings (`bw = 30`) seemed to correctly define features. It
-is however advisable to evaluate settings on multiple slices, ideally with
-signal from more than one compound being present. Such slices could be
-identified in e.g. a plot created with the `plotChromPeaks` function (see
-example in the chromatographic peak detection section).
+indicated with a grey rectangle in the lower panel of the plot. In the present
+example, because retention times of the two chromatographic peaks are very
+similar, this rectangle is very narrow and looks thus more like a vertical
+line. Based on this result, the default settings (`bw = 30`) seemed to correctly
+define features. It is however advisable to evaluate settings on multiple
+slices, ideally with signal from more than one compound being present. Such
+slices could be identified in e.g. a plot created with the `plotChromPeaks`
+function (see example in the chromatographic peak detection section).
 
 In our example we extract a chromatogram for an *m/z* slice containing signal
 for known isomers betaine and valine ([M+H]+ *m/z* 118.08625).
 
 ```{r correspondence-bw, fig.cap = "Correspondence analysis with default settings on an *m/z* slice containing signal from multiple ions."}
 #' Plot the chromatogram for an m/z slice containing betaine and valine
-mzr <- 118.08625 + c(-0.01, 0.01)
-chr_2 <- chromatogram(data, mz = mzr, aggregationFun = "max")
+mzr <- 118.08625 + c(-0.005, 0.005)
+chr_2 <- chromatogram(mse, mz = mzr, aggregationFun = "max")
 
 #' Correspondence in that slice using default settings
 plotChromPeakDensity(chr_2, param = pdp)
@@ -1527,14 +1528,14 @@ reduced value for parameter `bw`.
 
 ```{r correspondence-bw-fix, fig.cap = "Correspondence analysis with reduced bw setting on a *m/z* slice containing signal from multiple ions."}
 #' Reducing the bandwidth
-pdp <- PeakDensityParam(sampleGroups = sampleData(data)$group, bw = 1.8)
+pdp <- PeakDensityParam(sampleGroups = sampleData(mse)$group, bw = 1.8)
 plotChromPeakDensity(chr_2, param = pdp)
 ```
 
 Setting `bw = 1.8` strongly reduced the smoothness of the density curve
 resulting in a higher number of density *peaks* and hence a nice grouping of
 (aligned) chromatographic peaks into separate features. Note that the height of
-the peaks of the density curve are not considered for the grouping.
+the peaks of the density curve are not relevant for the grouping.
 
 By having defined a `bw` appropriate for our data set, we proceed and perform
 the correspondence analysis on the full data set. Other parameters of
@@ -1557,9 +1558,9 @@ allows to generate *m/z*-dependent bin sizes: the width of the *m/z* slices
 increases by `ppm` of the bin's *m/z* along the *m/z* axis.
 
 For our correspondence analysis we set the maximal acceptable difference of
-chrom peaks' *m/z* values with `binSize = 0.02` and `ppm = 10`, hence grouping
+chrom peaks' *m/z* values with `binSize = 0.01` and `ppm = 10`, hence grouping
 chromatographic peaks with similar retention time and with a difference of their
-*m/z* values that is smaller than 0.02 + 10 ppm of their *m/z* values. By
+*m/z* values that is smaller than 0.01 + 10 ppm of their *m/z* values. By
 setting `minFraction = 0.4` we in addition require for a feature that a
 chromatographic peak was detected in `>=` 40% of samples of at least one sample
 group.
@@ -1567,7 +1568,7 @@ group.
 ```{r correspondence-analysis}
 #' Set in addition parameter ppm to a value of 10
 pdp <- PeakDensityParam(sampleGroups = sampleData(mse)$group, bw = 1.8,
-                        minFraction = 0.4, binSize = 0.02, ppm = 10)
+                        minFraction = 0.4, binSize = 0.01, ppm = 10)
 
 #' Perform the correspondence analysis on the full data
 mse <- groupChromPeaks(mse, param = pdp)
@@ -1821,9 +1822,9 @@ l <- lm(log2(avg_filled) ~ log2(avg_detect))
 summary(l)
 ```
 
-With a value of 0.994, the slope of the line is thus very close to the slope of
+With a value of 1.007, the slope of the line is thus very close to the slope of
 the identity line and the two sets of values are also highly correlated (R
-squared of 0.79).
+squared of 0.81).
 
 
 
@@ -1977,8 +1978,8 @@ available in the infrastructure provided through the *xcms*, *Spectra*,
 *MsCoreUtils*, *MetaboCoreUtils* and other related Bioconductor packages. It
 would for example be easily possible to extract specific information for
 selected chromatographic peaks or LC-MS features from an *xcms* result object
-and perform some additional visualizations or analyses on them. Below we first
-identify chromatographic peaks that would match the *m/z* of serine.
+and perform some additional visualizations or analyses on them. AS an example we
+below first identify chromatographic peaks that would match the *m/z* of serine.
 
 ```{r}
 #' Extract chromatographic peaks matching the m/z of the [M+H]+ of serine
@@ -2004,10 +2005,10 @@ serine_ms1_2 <- chromPeakSpectra(mse, msLevel = 1, method = "closest_rt",
                                  peaks = rownames(serine_pks)[2])
 ```
 
-For LC-MS/MS data, this function would allow to select all MS2 spectra from the
-data set with their precursor m/z (and retention time) within the
-chromatographic peak's *m/z* and retention time width using parameters `msLevel
-= 2` and `method = "all"`.
+For LC-MS/MS data, this function would also allow to extract all MS2 spectra
+from the data set with their precursor m/z (and retention time) within the
+chromatographic peak's *m/z* and retention time width by using parameters
+`msLevel = 2` and `method = "all"`.
 
 Below we plot the EIC and the MS1 scan for the selected chromatographic peak.
 
@@ -2033,9 +2034,9 @@ and retention time ranges of the chromatographic peak in that sample,
 `featureChrommatograms` will instead integrate the signal from the *m/z* and
 retention time area of the **feature**, i.e. will use a single area and
 integrate the signal from that same area in each sample. This *m/z* - retention
-time area might eventually be larger than the respective ranges for a single
+time area might however be larger than the respective ranges for a single
 chromatographic peak in one sample. This *m/z* - retention time area for
-features can be extracted using the `featureArea` function:
+features can also be extracted (and evaluated) using the `featureArea` function:
 
 ```{r}
 #' Extract the m/z - retention time area for features
@@ -2075,10 +2076,10 @@ cols[iso_idx[[1]]] <- "#ff0000ff"
 plotSpectra(serine_ms1_2, col = cols, lwd = 2)
 ```
 
-While in the example above were specifically looking for potential isotopes of a
-single, selected, mass peak (by setting `seedMz` to the *m/z* value of that
-peak), we could also use `isotopologues` to identify all potential isotope
-groups in a spectrum.
+While in the example above we were specifically looking for potential isotopes
+of a single, selected, mass peak (by setting `seedMz` to the *m/z* value of that
+peak), we could also use `isotopologues` without specifying `seedMz` to identify
+all potential isotope groups in a spectrum.
 
 ```{r}
 #' Identify all potential isotope peaks in the MS1 spectrum
@@ -2151,7 +2152,7 @@ space from an LC-MS experiment.
 
 We below subset the data to the first sample and visualize the identified
 chromatographic peaks in the *m/z* - retention time plane using the
-`plotChromPeaks` function already used before.
+`plotChromPeaks` function that we used already before.
 
 ```{r, fig.cap = "Position of identified chromatographic peaks in the first sample."}
 #' Plot identified chromatographic peaks in the first sample
@@ -2267,10 +2268,10 @@ particular how to adapt peak detection setting on a rather noisy
 *chromatographic* data. Below we load the example data from a text file.
 
 ```{r peaks-load}
-data <- read.table(
+cdata <- read.table(
     system.file("txt", "chromatogram.txt", package = "xcmsTutorials"),
     sep = "\t", header = TRUE)
-head(data)
+head(cdata)
 ```
 
 Our data has two columns, one with *retention times* and one with
@@ -2278,7 +2279,7 @@ Our data has two columns, one with *retention times* and one with
 data.
 
 ```{r peaks-plot, fig.width = 12, fig.height = 2.15}
-chr <- Chromatogram(rtime = data$rt, intensity = data$intensity)
+chr <- Chromatogram(rtime = cdata$rt, intensity = cdata$intensity)
 par(mar = c(2, 2, 0, 0))
 plot(chr)
 ```

From 38c04b673057ae68104d9c60995a41725f8bde88 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Mon, 19 Feb 2024 08:10:10 +0100
Subject: [PATCH 32/42] Update readme

---
 DESCRIPTION | 2 +-
 README.md   | 8 +++++---
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index e86b82a..ca4443c 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -22,7 +22,7 @@ LazyData: false
 Roxygen: list(markdown = TRUE)
 Depends:
     R (>= 4.3),
-    xcms (>= 3.99.4),
+    xcms (>= 4.1.5),
     Spectra (>= 1.11.10),
     MsExperiment,
     msdata
diff --git a/README.md b/README.md
index 5b10cf8..dbdd8f8 100644
--- a/README.md
+++ b/README.md
@@ -55,8 +55,10 @@ required steps for installation are:
 - If you don't already have, install [docker](https://www.docker.com/). Find
   installation information [here](https://docs.docker.com/desktop/).
 - Get the [docker image](https://hub.docker.com/r/jorainer/xcms_tutorials) of
-  this tutorial with `docker pull jorainer/xcms_tutorials:latest`.
-- Start docker using
+  this tutorial e.g. from the command line with `docker pull
+  jorainer/xcms_tutorials:latest`.
+- Start the docker container, either through the Docker Desktop, or on the
+  command line with
 ```
   docker run \
       -e PASSWORD=bioc \
@@ -72,7 +74,7 @@ required steps for installation are:
 
 For manual installation, an R version >= 4.3.0 is required as well as recent
 versions of the packages `MsExperiment`, `Spectra` and in particular the `xcms`
-(version >= 3.99.0 is needed). These can be installed using the code below:
+(version >= 4.1.0 is needed). These can be installed using the code below:
 
 ```r
 install.packages("BiocManager")

From 94b71a3ec85f2460d833a8c9d2e3e752d62f20fb Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Fri, 19 Apr 2024 13:11:07 +0200
Subject: [PATCH 33/42] Quick fix for current devel packages

---
 Dockerfile                       | 2 +-
 vignettes/xcms-preprocessing.Rmd | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/Dockerfile b/Dockerfile
index 2f9e7ae..2ac0117 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -11,6 +11,6 @@ WORKDIR /home/rstudio
 COPY --chown=rstudio:rstudio . /home/rstudio/
 
 ## Install the xcmsTutorials package and additional required packages
-RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); BiocManager::install(ask = FALSE); BiocManager::install('sneumann/xcms', ref = 'jomain')"
+RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); BiocManager::install(ask = FALSE); BiocManager::install('sneumann/xcms')"
 
 RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); devtools::install('.', dependencies = TRUE, build_vignettes = TRUE, repos = BiocManager::repositories())"
diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index f365c02..28c8691 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -2279,6 +2279,7 @@ Our data has two columns, one with *retention times* and one with
 data.
 
 ```{r peaks-plot, fig.width = 12, fig.height = 2.15}
+library(MSnbase)
 chr <- Chromatogram(rtime = cdata$rt, intensity = cdata$intensity)
 par(mar = c(2, 2, 0, 0))
 plot(chr)

From 3395aab6b5584c9d91ec4742ab0aecdf5ef8cd96 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Fri, 19 Apr 2024 13:46:20 +0200
Subject: [PATCH 34/42] Update dockerfile

---
 Dockerfile | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Dockerfile b/Dockerfile
index 2ac0117..37356ba 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -11,6 +11,6 @@ WORKDIR /home/rstudio
 COPY --chown=rstudio:rstudio . /home/rstudio/
 
 ## Install the xcmsTutorials package and additional required packages
-RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); BiocManager::install(ask = FALSE); BiocManager::install('sneumann/xcms')"
+RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); BiocManager::install(ask = FALSE); BiocManager::install('xcms', ask = FALSE, type = 'source')"
 
-RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); devtools::install('.', dependencies = TRUE, build_vignettes = TRUE, repos = BiocManager::repositories())"
+RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); devtools::install('.', dependencies = TRUE, type = 'source', build_vignettes = TRUE, repos = BiocManager::repositories())"

From 1f12911729defbdc7368b30f6e4dc0010177466c Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Fri, 19 Apr 2024 14:19:06 +0200
Subject: [PATCH 35/42] Add MSnbase to Suggests

---
 DESCRIPTION                      | 1 +
 vignettes/xcms-preprocessing.Rmd | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index ca4443c..8d77870 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -29,6 +29,7 @@ Depends:
 Suggests:
     MetaboCoreUtils,
     MsCoreUtils,
+    MSnbase,
     png,
     BiocStyle,
     knitr,
diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index 28c8691..95f6e03 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -15,7 +15,7 @@ vignette: >
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding[utf8]{inputenc}
   %\VignettePackage{xcmsTutorials}
-  %\VignetteDepends{xcms,Spectra,BiocStyle,MetaboCoreUtils,knitr,png,MsCoreUtils,msdata,MsExperiment,pheatmap}
+  %\VignetteDepends{xcms,Spectra,BiocStyle,MetaboCoreUtils,knitr,png,MsCoreUtils,msdata,MsExperiment,pheatmap,MSnbase}
 bibliography: references.bib
 ---
 

From a209a41e8b618f26f35b1af8724f663c78a9a723 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Fri, 19 Apr 2024 16:24:53 +0200
Subject: [PATCH 36/42] Force installation of xcms from github

---
 Dockerfile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Dockerfile b/Dockerfile
index 37356ba..d75e338 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -11,6 +11,6 @@ WORKDIR /home/rstudio
 COPY --chown=rstudio:rstudio . /home/rstudio/
 
 ## Install the xcmsTutorials package and additional required packages
-RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); BiocManager::install(ask = FALSE); BiocManager::install('xcms', ask = FALSE, type = 'source')"
+RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); BiocManager::install(ask = FALSE, type = 'source'); BiocManager::install(c('RCurl', 'xcms'), ask = FALSE, dependencies = TRUE, type = 'source'); BiocManager::install('sneumann/xcms')"
 
 RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); devtools::install('.', dependencies = TRUE, type = 'source', build_vignettes = TRUE, repos = BiocManager::repositories())"

From 998baf8fa696b0102befcc03b4cf2cba09a7f8e7 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Mon, 13 May 2024 12:25:38 +0200
Subject: [PATCH 37/42] Use Bioconductor RELEASE_3_19 docker image

---
 .github/workflows/build_deploy.yaml |  2 +-
 .github/workflows/docker-push.yaml  |  2 +-
 DESCRIPTION                         |  5 +++--
 Dockerfile                          |  6 +++---
 NEWS.md                             |  2 +-
 README.md                           | 17 ++++++++++-------
 6 files changed, 19 insertions(+), 15 deletions(-)

diff --git a/.github/workflows/build_deploy.yaml b/.github/workflows/build_deploy.yaml
index 7212c08..aeb5124 100644
--- a/.github/workflows/build_deploy.yaml
+++ b/.github/workflows/build_deploy.yaml
@@ -7,7 +7,7 @@ on:
 jobs:
   build-and-deploy:
     runs-on: ubuntu-latest
-    container: jorainer/xcms_tutorials:latest
+    container: jorainer/xcms_tutorials:RELEASE_3_19
     steps:
       ## Most of these steps are the same as the ones in
       ## https://github.com/r-lib/actions/blob/master/examples/check-standard.yaml
diff --git a/.github/workflows/docker-push.yaml b/.github/workflows/docker-push.yaml
index 0f02eaa..9814951 100644
--- a/.github/workflows/docker-push.yaml
+++ b/.github/workflows/docker-push.yaml
@@ -32,7 +32,7 @@ jobs:
           push: true
           tag_with_ref: true
           tag_with_sha: true
-          tags: jorainer/xcms_tutorials:latest
+          tags: jorainer/xcms_tutorials:RELEASE_3_19
       -
         name: Image digest
         run: echo ${{ steps.docker_build.outputs.digest }}
diff --git a/DESCRIPTION b/DESCRIPTION
index 8d77870..39faee5 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -36,9 +36,10 @@ Suggests:
     rmarkdown,
     RColorBrewer,
     SummarizedExperiment,
-    pheatmap
+    pheatmap,
+    RCurl
 URL: https://jorainer.github.io/xcmsTutorials/
 BugReports: https://github.com/jorainer/xcmsTutorials/issues/new
 VignetteBuilder: knitr
 RoxygenNote: 7.2.3
-DockerImage: jorainer/xcms_tutorials:latest
+DockerImage: jorainer/xcms_tutorials:RELEASE_3_19
diff --git a/Dockerfile b/Dockerfile
index d75e338..b70cf66 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,9 +1,9 @@
-FROM bioconductor/bioconductor_docker:devel
+FROM bioconductor/bioconductor_docker:RELEASE_3_19
 
 LABEL name="jorainer/xcms_tutorials" \
       url="https://github.com/jorainer/xcmsTutorials" \
       maintainer="johannes.rainer@eurac.edu" \
-      description="Docker container to run xcms tutorials." \
+      description="Docker container to run xcms tutorials. This version bases on the Bioconductor release 3.19 docker image." \
       license="Artistic-2.0"
 
 WORKDIR /home/rstudio
@@ -11,6 +11,6 @@ WORKDIR /home/rstudio
 COPY --chown=rstudio:rstudio . /home/rstudio/
 
 ## Install the xcmsTutorials package and additional required packages
-RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); BiocManager::install(ask = FALSE, type = 'source'); BiocManager::install(c('RCurl', 'xcms'), ask = FALSE, dependencies = TRUE, type = 'source'); BiocManager::install('sneumann/xcms')"
+RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); BiocManager::install(ask = FALSE, type = 'source'); BiocManager::install(c('RCurl', 'xcms'), ask = FALSE, dependencies = TRUE, type = 'source')"
 
 RUN Rscript -e "options(repos = c(CRAN = 'https://cran.r-project.org')); devtools::install('.', dependencies = TRUE, type = 'source', build_vignettes = TRUE, repos = BiocManager::repositories())"
diff --git a/NEWS.md b/NEWS.md
index d5e8acf..76ba7e2 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -2,7 +2,7 @@
 
 ## Changes in 1.1.0
 
-- Use Bioconductor 3.19 devel release.
+- Use Bioconductor 3.19 release docker image and packages.
 - Add examples for simple quality assessment of BPC or BPS data.
 - Introduce parameter `ppm` for `PeakDensityParam` correspondence analysis
   method.
diff --git a/README.md b/README.md
index dbdd8f8..1643e50 100644
--- a/README.md
+++ b/README.md
@@ -49,21 +49,24 @@ The workshop files along with an R runtime environment including all required
 packages and the RStudio (Posit) editor are all bundled in a *docker*
 container. After installation, this docker container can be run on the computer
 and the code and examples from the workshop can be evaluated within this
-environment (without the need to install any additional packages or files). The
-required steps for installation are:
+environment (without the need to install any additional packages or files).
+
+This version of the workshop uses packages from **Bioconductor release 3.19**
+(May 2024) and hence bases on Bioconductor's docker container for that release
+(*RELEASE_3_19*). The required steps for installation are:
 
 - If you don't already have, install [docker](https://www.docker.com/). Find
   installation information [here](https://docs.docker.com/desktop/).
 - Get the [docker image](https://hub.docker.com/r/jorainer/xcms_tutorials) of
   this tutorial e.g. from the command line with `docker pull
-  jorainer/xcms_tutorials:latest`.
+  jorainer/xcms_tutorials:RELEASE_3_19`.
 - Start the docker container, either through the Docker Desktop, or on the
   command line with
 ```
   docker run \
       -e PASSWORD=bioc \
       -p 8787:8787 \
-      jorainer/xcms_tutorials:latest
+      jorainer/xcms_tutorials:RELEASE_3_19
 ```
 
 - Enter `http://localhost:8787` in a web browser and log in with username
@@ -72,9 +75,9 @@ required steps for installation are:
   the *vignettes* folder and evaluate the R code blocks in that document.
 
 
-For manual installation, an R version >= 4.3.0 is required as well as recent
-versions of the packages `MsExperiment`, `Spectra` and in particular the `xcms`
-(version >= 4.1.0 is needed). These can be installed using the code below:
+For manual installation, an R version >= 4.4.0 is required as well as recent
+versions of the packages `MsExperiment`, `Spectra` and in particular the
+`xcms`. These can be installed using the code below:
 
 ```r
 install.packages("BiocManager")

From acc1f6aad7096b2ac9a36c619eea790e29e537d7 Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Mon, 13 May 2024 14:34:33 +0200
Subject: [PATCH 38/42] docs: add zenodo DOI

---
 NEWS.md   | 1 +
 README.md | 3 +++
 2 files changed, 4 insertions(+)

diff --git a/NEWS.md b/NEWS.md
index 76ba7e2..be8c359 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -2,6 +2,7 @@
 
 ## Changes in 1.1.0
 
+- Release the tutorial to Zenodo.
 - Use Bioconductor 3.19 release docker image and packages.
 - Add examples for simple quality assessment of BPC or BPS data.
 - Introduce parameter `ppm` for `PeakDensityParam` correspondence analysis
diff --git a/README.md b/README.md
index 1643e50..6055184 100644
--- a/README.md
+++ b/README.md
@@ -42,6 +42,9 @@ packages). The R command `rmarkdown::render("xcms-preprocessing.Rmd")` would
 generate the html file
 [xcms-preprocessing.html](https://jorainer.github.io/xcmsTutorials/xcms-preprocessing.html).
 
+## To cite
+
+- Version 1.1.0 (for Bioconductor release 3.19, May 2024): [![DOI](https://zenodo.org/badge/645255717.svg)](https://zenodo.org/doi/10.5281/zenodo.11185520)
 
 ## Installation
 

From f558a7235a048a18da9ba1677a47141994a242cc Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Fri, 17 May 2024 16:40:37 +0200
Subject: [PATCH 39/42] Add docker image reference

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 6055184..8cb06a8 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,9 @@
 # Exploring and analyzing LC-MS data with *Spectra* and *xcms*
 
 [![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc/4.0/)
+![docker pulls](https://img.shields.io/docker/pulls/jorainer/xcms_tutorials)
+[![Docker Image Version (latest by date)](https://img.shields.io/docker/v/jorainer/xcms_tutorials?label=docker%20image)](https://hub.docker.com/repository/docker/jorainer/xcms_tutorials)
+[![DOI](https://zenodo.org/badge/645255717.svg)](https://zenodo.org/doi/10.5281/zenodo.11185520)
 
 ![xcms](man/figures/xcms.png)
 ![MsExperiment](man/figures/MsExperiment.png)
@@ -42,9 +45,6 @@ packages). The R command `rmarkdown::render("xcms-preprocessing.Rmd")` would
 generate the html file
 [xcms-preprocessing.html](https://jorainer.github.io/xcmsTutorials/xcms-preprocessing.html).
 
-## To cite
-
-- Version 1.1.0 (for Bioconductor release 3.19, May 2024): [![DOI](https://zenodo.org/badge/645255717.svg)](https://zenodo.org/doi/10.5281/zenodo.11185520)
 
 ## Installation
 

From 21314c7757b80887571f7468f0c4ec8b8377613a Mon Sep 17 00:00:00 2001
From: jorainer <johannes.rainer@gmail.com>
Date: Fri, 7 Jun 2024 10:19:02 +0200
Subject: [PATCH 40/42] refactor: use spectraSampleIndex and use () for
 functions

- Use the `spectraSampleIndex()` function instead of `fromFile()`.
- Use `()` to indicate functions in the descriptive text to easily discriminate
  them from classes and parameters.
---
 DESCRIPTION                      |   2 +-
 NEWS.md                          |   7 +
 vignettes/xcms-preprocessing.Rmd | 274 ++++++++++++++++---------------
 3 files changed, 148 insertions(+), 135 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 39faee5..afd45d2 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: xcmsTutorials
 Title: Exploring and Analyzing LC-MS data with Spectra and xcms
-Version: 1.1.0
+Version: 1.1.1
 Authors@R: c(
 	     person(given = "Johannes", family = "Rainer",
 		    email = "Johannes.Rainer@eurac.edu",
diff --git a/NEWS.md b/NEWS.md
index be8c359..d8ef24d 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,12 @@
 # xcmsTutorials 1.0
 
+## Changes in 1.1.1
+
+- Use `spectraSampleIndex()` instead of the old `fromFile()` function to get
+  sample assignments of individual spectra.
+- Add `()` to all functions in the descriptive text to discriminate functions
+  from classes or parameter names.
+
 ## Changes in 1.1.0
 
 - Release the tutorial to Zenodo.
diff --git a/vignettes/xcms-preprocessing.Rmd b/vignettes/xcms-preprocessing.Rmd
index 95f6e03..db8480a 100644
--- a/vignettes/xcms-preprocessing.Rmd
+++ b/vignettes/xcms-preprocessing.Rmd
@@ -150,7 +150,7 @@ suggested to provide all experiment-relevant phenotypic and technical
 information through such a data frame. Also, the data frame could be defined in
 an xls sheet that could then be imported with the `read_xlsx` function from the
 *readxl* R package. This data frame is then passed, along with the file names,
-to the `readMsExperiment` call to import the data.
+to the `readMsExperiment()` call to import the data.
 
 ```{r load-data}
 #' Load required libraries
@@ -178,7 +178,7 @@ The MS data of the experiment is now *represented* by an `MsExperiment` object.
 ## Basic data access
 
 The `MsExperiment` object manages the *linkage* between samples and spectra.
-The `length` of an `MsExperiment` is defined by the number of samples (files)
+The `length()` of an `MsExperiment` is defined by the number of samples (files)
 within the object.
 
 ```{r general-access}
@@ -198,7 +198,7 @@ mse_2
 
 This did subset the full data, including sample information and spectra data
 to those of the second file. Phenotype information can be retrieved with the
-`sampleData` function from an `MsExperiment` object.
+`sampleData()` function from an `MsExperiment` object.
 
 ```{r}
 #' Extract sample information
@@ -206,7 +206,7 @@ sampleData(mse_2)
 ```
 
 The MS data is stored as a `Spectra` object within the `MsExperiment` and can be
-accessed using the `spectra` function.
+accessed using the `spectra()` function.
 
 ```{r show-fData}
 #' Access the MS data
@@ -245,13 +245,14 @@ contains all spectra from the experiment, first all spectra from the first file,
 followed by the spectra from the second. The mapping of spectra to samples is
 defined in the `MsExperiment` object. To access spectra from a specific sample
 we either subset the `MsExperiment` to that particular sample (as done in the
-example above) or we use the `fromFile` function that returns for each spectrum
-the index of the file within the `MsExperiment` to which it belongs. Below we
-use `fromFile` to determine the total number of spectra per sample.
+example above) or we use the `spectraSampleIndex()` function that returns for
+each spectrum the index of the file within the `MsExperiment` to which it
+belongs. Below we use `spectraSampleIndex()` to determine the total number of
+spectra per sample.
 
 ```{r}
 #' Get the number of spectra per file.
-fromFile(mse) |>
+spectraSampleIndex(mse) |>
     table()
 ```
 
@@ -261,7 +262,8 @@ of spectra.
 
 Besides the peak data (*m/z* and intensity values) also additional spectra
 variables (metadata) are available in a `Spectra` object. These can be listed
-using the `spectraVariables` function that we call on our example MS data below.
+using the `spectraVariables()` function that we call on our example MS data
+below.
 
 ```{r}
 #' List available spectra variables
@@ -274,7 +276,7 @@ Thus, for all spectra we have general information such as the MS level
 spectra variables dedicated accessor functions are available (such as `msLevel`,
 `rtime`). In addition it is possible to access any variable using `$` and the
 name of the variable (similar to accessing the columns of a `data.frame`). As an
-example we extract below the `msLevel` spectra variable and use the `table`
+example we extract below the `msLevel` spectra variable and use the `table()`
 function on the result to get an overview of the number of spectra from
 different MS levels available in the object.
 
@@ -288,9 +290,9 @@ spectra(mse) |>
 The present data set contains thus 1,862 spectra, all from MS level 1.
 
 We could also check the number of peaks per spectrum in the different data
-files. The number of peaks per spectrum can be extracted with the `lengths`
+files. The number of peaks per spectrum can be extracted with the `lengths()`
 function. Below we extract these values, split them by file and then calculate
-the quartiles of the peak counts using the `quantile` function.
+the quartiles of the peak counts using the `quantile()` function.
 
 ```{r}
 #' Get the distribution of peak counts per file
@@ -305,7 +307,7 @@ Thus, for the present data set, the number of spectra and also the average
 number of peaks per spectra are comparable.
 
 Individual MS spectra can be accessed by subsetting the `Spectra` object
-returned by `spectra`. As an example we below subset the data to the second
+returned by `spectra()`. As an example we below subset the data to the second
 sample, extract the spectra from that sample and subset to the spectrum number
 123.
 
@@ -315,9 +317,9 @@ sp <- spectra(mse[2])[123]
 sp
 ```
 
-*m/z* and intensity values can be extracted from a `Spectra` using the `mz` and
-`intensity` functions that (always) return a list of `numeric` vectors with the
-respective values:
+*m/z* and intensity values can be extracted from a `Spectra` using the `mz()`
+and `intensity()` functions that (always) return a list of `numeric` vectors
+with the respective values:
 
 ```{r}
 #' Extract m/z values
@@ -327,7 +329,7 @@ mz(sp)
 intensity(sp)
 ```
 
-As an alternative, the `peaksData` function could be used to extract both the
+As an alternative, the `peaksData()` function could be used to extract both the
 *m/z* and intensity values (as two-column numeric matrix) with a single function
 call.
 
@@ -343,7 +345,7 @@ intensity(sp) |>
 
 The same operation can also be applied to the full data set. As an example we
 calculate below the total ion signal for each spectrum in the first file and
-determine the distribution of these using the `quantile` function.
+determine the distribution of these using the `quantile()` function.
 
 ```{r}
 #' Calculate the distribution of total ion signal of the first file
@@ -385,7 +387,7 @@ BPC from our data. The BPC extracts the maximum peak signal from each spectrum
 in a data file and allows thus to plot this information (on the y-axis) against
 the retention time for that spectrum. While we could also extract these values
 similarly to the total ion intensity in the previous section, we use below the
-`chromatogram` function that allows extraction of chromatographic data from MS
+`chromatogram()` function that allows extraction of chromatographic data from MS
 data (e.g. from an `MsExperiment` object). With parameter `aggregationFun =
 "max"` we define to report the maximum signal per spectrum (setting
 `aggregationFun = "sum"` would in contrast sum up all intensities of a spectrum
@@ -420,7 +422,7 @@ bpc_bin <- bin(bpc, binSize = 1)
 
 After binning, the two chromatograms have the same retention times (and number
 of intensities) and we can thus *bind* their intensity vectors as columns of a
-new numeric matrix using `cbind`:
+new numeric matrix using `cbind()`:
 
 ```{r}
 #' Create an intensity matrix
@@ -484,7 +486,7 @@ plotSpectra(spectra(mse)[123:124], xlim = c(105, 130))
 
 These two spectra could now be merged by reporting for each *m/z* (or rather for
 peaks with very similar *m/z* in consecutive spectra) the maximal signal
-observed. In *Spectra*, the `combineSpectra` function allows to
+observed. In *Spectra*, the `combineSpectra()` function allows to
 aggregate/combine sets of spectra into a single spectrum. By default, this
 function will combine sets of spectra (that can be defined with parameter `f`)
 creating an union of the peaks present in spectra of a set. For mass peaks with
@@ -496,7 +498,7 @@ containing mass peaks present in any of the spectra of that file. Mass peaks
 with a difference in their *m/z* that is smaller than `ppm` (parts-per-million
 of the *m/z* value) are combined into one peak for which the maximal intensity
 of the grouped peaks is reported. Note that it is suggested to use a small value
-for `ppm` to combine MS1 spectra with `combineSpectra`.
+for `ppm` to combine MS1 spectra with `combineSpectra()`.
 
 ```{r}
 #' Combine all spectra of one file into a single spectrum
@@ -528,7 +530,7 @@ do.call(cbind, intensity(bps_bin)) |>
 ```
 
 Alternatively, we can also directly calculate the similarity between the base
-peak spectra using the `compareSpectra` function and one of the available peak
+peak spectra using the `compareSpectra()` function and one of the available peak
 similarity measures. Below we use the normalized dot product to calculate the
 similarity between the two spectra matching peaks using an *m/z* tolerance of 10
 ppm.
@@ -551,8 +553,8 @@ in serum samples (such as ions of the molecule serine). With the particular
 LC-MS setup used for the present samples, ions for this metabolite are expected
 to elute at about 180 seconds (this retention time was determined by measuring a
 pure standard for this compound on the same LC-MS setup). We thus filter below
-the spectra data using the `filterRt` function extracting only spectra measured
-between 180 and 181 seconds.
+the spectra data using the `filterRt()` function extracting only spectra
+measured between 180 and 181 seconds.
 
 ```{r}
 #' Extract all spectra measured between 180 and 181 seconds
@@ -573,14 +575,14 @@ the originating sample, but that would involve additional R code.
 basename(dataOrigin(sps))
 ```
 
-Alternatively, we could use the `filterSpectra` function on the `MsExperiment`
-object passing the filter function (in our case `filterRt`) to that
+Alternatively, we could use the `filterSpectra()` function on the `MsExperiment`
+object passing the filter function (in our case `filterRt()`) to that
 function. This filters the `Spectra` object *within* the `MsExperiment`
 retaining all associations (links) between samples and subset spectra. While
-some of the most commonly used filter functions, such as `filterRt` or
-`filterMsLevel`, are also implemented for `MsExperiment`, the `filterSpectra`
-function allows to apply any of the many filter functions available for
-`Spectra` objects to the data.
+some of the most commonly used filter functions, such as `filterRt()` or
+`filterMsLevel()`, are also implemented for `MsExperiment`, the
+`filterSpectra()` function allows to apply any of the many filter functions
+available for `Spectra` objects to the data.
 
 ```{r}
 #' Subset the whole MsExperiment
@@ -602,7 +604,7 @@ at an *m/z* of about 130 and the second largest at about 106, which
 could represent signal for an ion of
 [Serine](https://en.wikipedia.org/wiki/Serine). Below we calculate the exact
 (monoisotopic) mass for serine from its chemical formula *C3H7NO3* using the
-`calculateMass` function from the `r Biocpkg("MetaboCoreUtils")` package.
+`calculateMass()` function from the `r Biocpkg("MetaboCoreUtils")` package.
 
 ```{r}
 #' Calculate the (monoisotopic) mass of serine
@@ -616,13 +618,13 @@ by mass spectrometry. In order to be detectable, molecules need to be ionized
 before being injected in an MS instrument. While different ions can (and will)
 be generated for a molecule, one of the most commonly generated ions in positive
 polarity is the *[M+H]+* ion (protonated ion). To calculate the *m/z* values for
-specific ions/adducts of molecules, we can use the `mass2mz` function, also from
-the *MetaboCoreUtils* package. Below we calculate the *m/z* for the *[M+H]+* ion
-of serine providing the monoisotopic mass of that molecule and specifying the
-adduct we are interested in. Also other types of adducts are supported. These
-could be listed with the `adductNames` function (`adductNames()` for all
-positively charged and `adductNames("negative")` for all negatively charge
-ions).
+specific ions/adducts of molecules, we can use the `mass2mz()` function, also
+from the *MetaboCoreUtils* package. Below we calculate the *m/z* for the
+*[M+H]+* ion of serine providing the monoisotopic mass of that molecule and
+specifying the adduct we are interested in. Also other types of adducts are
+supported. These could be listed with the `adductNames` function
+(`adductNames()` for all positively charged and `adductNames("negative")` for
+all negatively charge ions).
 
 ```{r}
 #' Calculate the m/z for the [M+H]+ ion of serine
@@ -630,9 +632,9 @@ serine_mz <- mass2mz(mass_serine, "[M+H]+")
 serine_mz
 ```
 
-The `mass2mz` function **always** returns a `matrix` with columns reporting the
-*m/z* for the requested adduct(s) of the molecule(s) which are available in the
-rows. Since we requested a single ion we reduce this `matrix` to a single
+The `mass2mz()` function **always** returns a `matrix` with columns reporting
+the *m/z* for the requested adduct(s) of the molecule(s) which are available in
+the rows. Since we requested a single ion we reduce this `matrix` to a single
 `numeric` value.
 
 ```{r}
@@ -640,10 +642,10 @@ serine_mz <- serine_mz[1, 1]
 ```
 
 We can now use this information to subset the MS data to the signal recorded for
-all ions with that particular *m/z*. We use again the `chromatogram` function
+all ions with that particular *m/z*. We use again the `chromatogram()` function
 and provide the *m/z* range of interest with the `mz` parameter of that
 function. Note that alternatively we could also first filter the data set by
-*m/z* using the `filterMzRange` function and then extract the chromatogram.
+*m/z* using the `filterMzRange()` function and then extract the chromatogram.
 
 ```{r, fig.cap = "Ion trace for an ion of serine"}
 #' Extract a full RT chromatogram for ions with an m/z similar than serine
@@ -657,7 +659,7 @@ retention time of a molecule for a specific LC-MS setup is not known beforehand,
 extracting such chromatograms for the *m/z* of interest and the full retention
 time range can help determining its likely retention time.
 
-The object returned by the `chromatogram` function arranges the individual
+The object returned by the `chromatogram()` function arranges the individual
 `MChromatogram` objects (each representing the chromatographic data consisting
 of pairs of retention time and intensity values of one sample) in a
 two-dimensional array, columns being samples (files) and rows data slices (i.e.,
@@ -665,9 +667,9 @@ two-dimensional array, columns being samples (files) and rows data slices (i.e.,
 `r Biocpkg("MSnbase")` package, is likely to be replaced in future with a more
 efficient and flexible data structure similar to `Spectra`.
 
-Data from the individual chromatograms can be accessed using the `intensity` and
-`rtime` functions (similar to the `mz` and `intensity` functions for a `Spectra`
-object).
+Data from the individual chromatograms can be accessed using the `intensity()`
+and `rtime()` functions (similar to the `mz()` and `intensity()` functions for a
+`Spectra` object).
 
 ```{r chromatogram}
 #' Get intensity values for the chromatogram of the first sample
@@ -686,8 +688,8 @@ retention time (i.e. in a spectrum).
 At last we further focus on the tentative signal of serine extracting the ion
 chromatogram restricting on the retention time range containing its
 signal. While we could also pass the retention time and *m/z* range with
-parameters `rt` and `mz` to the `chromatogram` function we instead filter the
-whole experiment by retention time and *m/z* before calling `chromatogram` on
+parameters `rt` and `mz` to the `chromatogram()` function we instead filter the
+whole experiment by retention time and *m/z* before calling `chromatogram()` on
 the such created data subset. With the example code below we thus create an
 extracted ion chromatogram (EIC, sometimes also referred to as XIC) for the
 *[M+H]+* ion of serine.
@@ -740,7 +742,7 @@ Instead of a single peak, several mass peaks were recorded by the MS instrument
 with an *m/z* very close to the theoretical *m/z* for the *[M+H]+* ion of serine
 (indicated with a red dotted line).
 
-We can also visualize this information differently: the `plot` function for
+We can also visualize this information differently: the `plot()` function for
 `MsExperiment` generates a two-dimensional visualization of the
 three-dimensional LC-MS data: peaks are drawn at their respective location in
 the two-dimensional *m/z* *vs* retention time plane with their intensity being
@@ -793,8 +795,8 @@ mse |>
 The impact of the centroiding is clearly visible: each signal for an ion in a
 spectrum was reduced to a single data point. For more advanced centroiding
 options, that can also fine-tune the *m/z* value of the reported centroid, see
-the documentation of the `pickPeaks` function or the centroiding vignette of the
-`r Biocpkg("MSnbase")` package.
+the documentation of the `pickPeaks()` function or the centroiding vignette of
+the `r Biocpkg("MSnbase")` package.
 
 While we could now simply proceed with the data analysis, we below save the
 centroided MS data to mzML files to also illustrate how the *Spectra* package
@@ -808,7 +810,7 @@ lapply(basename(unique(dataOrigin(spectra(mse)))), function (z) {
 })
 ```
 
-We use the `export` function for data export of the centroided `Spectra`
+We use the `export()` function for data export of the centroided `Spectra`
 object. Parameter `backend` allows to specify the MS data backend that should be
 used for the export, and that will also define the data format (use
 `backend = MsBackendMzR()` to export data in mzML format). Parameter `file`
@@ -860,7 +862,7 @@ identifying and quantifying such signals as shown in the sketch below.
 ![Chromatographic peak detection](images/LCMS-data-peaks.png)
 
 Such peak detection can be performed with the `r Biocpkg("xcms")` package using
-its `findChromPeaks` function. Several peak detection algorithms are available
+its `findChromPeaks()` function. Several peak detection algorithms are available
 that can be selected and configured with their respective parameter objects:
 
 - `MatchedFilterParam` to perform peak detection as described in the original
@@ -904,7 +906,7 @@ res <- findChromPeaks(serine_chr, param = cwp)
 chromPeaks(res)
 ```
 
-The peak matrix returned by `chromPeaks` is empty, thus, with the default
+The peak matrix returned by `chromPeaks()` is empty, thus, with the default
 settings *centWave* failed to identify any chromatographic peak in the EIC for
 serine. The default values for the parameters are shown below:
 
@@ -955,9 +957,9 @@ noise. Alternatively, or in addition, reduce the value for the `snthresh`
 parameter for peak detection performed on EICs.
 
 With our data set-specific `peakwidth` we were able to detect the peak for
-serine (highlighted in grey in the plot above). We can now use the `chromPeaks`
-function to extract the information on identified chromatographic peaks from our
-object.
+serine (highlighted in grey in the plot above). We can now use the
+`chromPeaks()` function to extract the information on identified chromatographic
+peaks from our object.
 
 ```{r chromPeaks-chromatogram}
 #' Extract identified chromatographic peaks from the EIC
@@ -1053,7 +1055,7 @@ mse <- findChromPeaks(mse, param = cwp)
 ```
 
 The results form the chromatographic peak detection were added by the
-`findChromPeaks` to our `mse` variable which now is an `XcmsExperiment` object
+`findChromPeaks()` to our `mse` variable which now is an `XcmsExperiment` object
 that, by extending the `MsExperiment` class inherits all of its functionality
 and properties, but in addition contains also all *xcms* preprocessing results.
 
@@ -1062,7 +1064,7 @@ mse
 ```
 
 We can extract the results from the peak detection step (as above) with the
-`chromPeaks` function. The optional parameters `rt` and `mz` would allow to
+`chromPeaks()` function. The optional parameters `rt` and `mz` would allow to
 extract peak detection results for a specified *m/z* - retention time region. In
 our example we extract all chromatographic peaks between an *m/z* range from 106
 to 108 and a retention time from 150 to 190.
@@ -1099,7 +1101,7 @@ both files contain measurements from the same sample (the QC pool).
 
 As an additional visual quality assessment, we can also plot the location of the
 identified chromatographic peaks in the *m/z* - retention time space for each
-data file using the `plotChromPeaks` function.
+data file using the `plotChromPeaks()` function.
 
 ```{r plotChromPeaks, fig.cap = "Location of the identified chromatographic peaks in the *m/z* - rt space."}
 #' Plot the location of peaks in the m/z - rt plane
@@ -1137,7 +1139,7 @@ mz_rt
 
 For our example we however manually define *m/z* - retention time regions
 (similarly as it could be done for known compounds). Below we extract the EICs
-for these regions with the `chromatogram` function and subsequently plot
+for these regions with the `chromatogram()` function and subsequently plot
 them. Identified chromatographic peaks within the plotted regions will by
 default be highlighted in a semitransparent grey color.
 
@@ -1159,10 +1161,10 @@ failed to define chromatographic peaks containing the full signal in the lower
 row. In both cases, the signal was split into separate chromatographic peaks
 within the same sample. This is a common problem with *centWave* on such noisy
 and broad signals. We could either try to adapt the *centWave* settings and
-repeat the chromatographic peak detection or use the `refineChromPeaks` function
-that allows to post-process peak detection results and fix such problems (see
-also the documentation of the `refineChromPeaks` function for all possible
-refinement options).
+repeat the chromatographic peak detection or use the `refineChromPeaks()`
+function that allows to post-process peak detection results and fix such
+problems (see also the documentation of the `refineChromPeaks()` function for
+all possible refinement options).
 
 To fuse the wrongly split peaks in the second row, we use the
 `MergeNeighboringPeaksParam` algorithm that merges chromatographic peaks that
@@ -1203,9 +1205,9 @@ chromPeaks(mse)[, "sample"] |>
     table()
 ```
 
-Also, `refineChromPeaks` adds information on the peak refinement to the object's
-`chromPeakData` data frame which provides additional metadata information for
-each chromatographic peak:
+Also, `refineChromPeaks()` adds information on the peak refinement to the
+object's `chromPeakData()` data frame which provides additional metadata
+information for each chromatographic peak:
 
 ```{r}
 chromPeakData(mse)
@@ -1224,7 +1226,7 @@ While chromatography helps to better discriminate between analytes it is also
 affected by variances that lead to shifts in retention times between measurement
 runs. Such differences can usually already be seen in a base peak chromatogram
 or total ion chromatogram. We thus extract and plot below the BPC for our data
-set. In the `chromatogram` call, we set the optional parameter `chromPeaks =
+set. In the `chromatogram()` call, we set the optional parameter `chromPeaks =
 "none"` to avoid the additional extraction of all identified chromatographic
 peaks.
 
@@ -1251,7 +1253,7 @@ for an illustration).
 
 ![Alignment](images/alignment.png)
 
-In *xcms*, the alignment can be performed with the `adjustRtime` function and
+In *xcms*, the alignment can be performed with the `adjustRtime()` function and
 one of the available alignment algorithms, that can be selected, and configured,
 with the respective parameter objects:
 
@@ -1323,7 +1325,7 @@ pools) values `>= 0.9` can be used. Otherwise, values between 0.7 and 0.9 might
 be more advisable to ensure that a reasonable set of features are selected.
 
 To evaluate anchor peaks that would be selected based on the defined settings,
-we can also use the `adjustRtimePeakGroups` method:
+we can also use the `adjustRtimePeakGroups()` method:
 
 ```{r}
 #' Get the anchor peaks that would be selected
@@ -1358,7 +1360,7 @@ mse <- adjustRtime(mse, param = pgp)
 ```
 
 After an alignment it is suggested to evaluate its results using the
-`plotAdjustedRtime` function. This function plots the differences between
+`plotAdjustedRtime()` function. This function plots the differences between
 adjusted and raw retention times for each sample on the y-axis along the
 adjusted retention times on the x-axis (each line hence representing the
 retention time adjustment of one sample/file). Points indicate the position of
@@ -1383,7 +1385,7 @@ measured during the same measurement run. Also, features used for the alignment
 
 To evaluate the impact of the alignment we next also plot the BPC before and
 after alignment. In a similar way as before, we set `chromPeaks = "none"` in the
-`chromatogram` call to tell the function to **not** include any identified
+`chromatogram()` call to tell the function to **not** include any identified
 chromatographic peaks in the returned chromatographic data.
 
 ```{r bpc-raw-adjusted, fig.cap = "BPC before (top) and after (bottom) alignment."}
@@ -1422,12 +1424,12 @@ The serine peaks are also nicely aligned after retention time adjustment. Again,
 it is advisable to evaluate the impact of the alignment on several EICs, ideally
 also spread along the retention time range.
 
-Note that `adjustRtime`, in addition to the retention times of the individual
+Note that `adjustRtime()`, in addition to the retention times of the individual
 (MS1) spectra of all files, adjusted also the retention times of the identified
 chromatographic peaks, as well as retention times of possibly present MS2
 spectra. The adjusted retention times are stored as a new spectra variable
-`"rtime_adjusted"` in the result object's `Spectra`. The `rtime` function on the
-result object will by default return these (adjusted) values.
+`"rtime_adjusted"` in the result object's `Spectra`. The `rtime()` function on
+the result object will by default return these (adjusted) values.
 
 
 ## Correspondence
@@ -1438,9 +1440,9 @@ are grouped across samples to form the so called *LC-MS features*.
 
 ![Correspondence](images/correspondence2_03.png)
 
-In *xcms*, correspondence is performed using the `groupChromPeaks` function. The
-correspondence algorithm can be selected and configured with the respective
-parameter objects:
+In *xcms*, correspondence is performed using the `groupChromPeaks()`
+function. The correspondence algorithm can be selected and configured with the
+respective parameter objects:
 
 - `NearestPeaksParam`: performs peak grouping based on the proximity of
   chromatographic peaks from different samples in the *m/z* - retention time
@@ -1467,11 +1469,11 @@ grouped by the *peakDensity* method is shown in the sketch below.
 ![peak density](images/correspondence2_density.png)
 
 Settings for this algorithm can be best tested and optimized using the
-`plotChromPeakDensity` function on extracted chromatograms. We below extract a
+`plotChromPeakDensity()` function on extracted chromatograms. We below extract a
 chromatogram for a *m/z* slice containing signal for a *[M+H]+* ion of serine
 and evaluate the result from a *peakDensity* correspondence analysis using that
 function. We use the default settings (`bw = 30`) and use again the sample group
-assignment defined in `sampleData`.
+assignment defined in `sampleData()`.
 
 ```{r}
 #' Extract a chromatogram for a m/z range containing serine
@@ -1494,7 +1496,7 @@ there was one chromatographic peak identified in each sample at a retention time
 of about 180 seconds and these two peaks are thus shown. The black solid line
 represents the density estimation (i.e. distribution or retention times) of the
 identified chromatographic peaks along the retention time axis. The smoothness
-of this curve (which is created with the base R `density` function) is
+of this curve (which is created with the base R `density()` function) is
 configured with the parameter `bw`. The *peakDensity* algorithm assigns all
 chromatographic peaks within the same *peak* of this density estimation curve to
 the same feature. Chromatographic peaks assigned to the same feature are
@@ -1504,7 +1506,7 @@ similar, this rectangle is very narrow and looks thus more like a vertical
 line. Based on this result, the default settings (`bw = 30`) seemed to correctly
 define features. It is however advisable to evaluate settings on multiple
 slices, ideally with signal from more than one compound being present. Such
-slices could be identified in e.g. a plot created with the `plotChromPeaks`
+slices could be identified in e.g. a plot created with the `plotChromPeaks()`
 function (see example in the chromatographic peak detection section).
 
 In our example we extract a chromatogram for an *m/z* slice containing signal
@@ -1590,9 +1592,9 @@ Over 300 features were identified in our example data set. Again, it is
 suggested to evaluate the results on selected compounds/ions. We therefore
 extract below the chromatogram for the *m/z* range containing signals for
 betaine and valine. After a correspondence analysis also feature definitions are
-extracted by the `chromatogram` call and we can show the results from the actual
-correspondence analysis (based also on the settings that were used) by setting
-`simulate = FALSE` in the `plotChromPeakDensity` call.
+extracted by the `chromatogram()` call and we can show the results from the
+actual correspondence analysis (based also on the settings that were used) by
+setting `simulate = FALSE` in the `plotChromPeakDensity()` call.
 
 ```{r correspondence-evaluate, fig.cap = "Result of correspondence on an *m/z* slice containing the isomers valine and betaine."}
 #' Extract chromatogram including signal for betaine and valine
@@ -1626,10 +1628,11 @@ represent signal from multiple different ions/compounds).
 
 Similar to the peak detection and alignment results, also the results from the
 correspondence analysis were added to the `XcmsExperiment` object. These can be
-extracted with the `featureDefinitions` function, that extracts the *definition*
-of the LC-MS features and the `featureValues` function that extracts the
-numerical matrix with the feature abundances (in all samples). Below we extract
-the definition of the features and display the first 6 rows.
+extracted with the `featureDefinitions()` function, that extracts the
+*definition* of the LC-MS features and the `featureValues()` function that
+extracts the numerical matrix with the feature abundances (in all
+samples). Below we extract the definition of the features and display the first
+6 rows.
 
 ```{r correspondence-featureDefinitions}
 #' Definition of the features
@@ -1642,12 +1645,12 @@ Each row defines one feature and provides information on it's *m/z* (column
 list the minimum and maximum rt or *m/z* value of the chromatographic peaks
 assigned to the feature. Additional columns list the number of chromatographic
 peaks that were assigned to the feature and the MS level. Column `"peakidx"`
-provides the indices of the chromatographic peaks in the `chromPeaks` matrix
+provides the indices of the chromatographic peaks in the `chromPeaks()` matrix
 that were assigned to the feature - but generally users will not need or extract
 that information.
 
 The feature abundance matrix, which is the final result of the *xcms*
-preprocessing, can be extracted with the `featureValues` function. By default,
+preprocessing, can be extracted with the `featureValues()` function. By default,
 with parameter `method = "maxint"`, it returns for each feature the integrated
 peak signal of the chromatographic peak with the highest signal per sample. Note
 that this has only an effect for features with more than one chromatographic
@@ -1712,9 +1715,10 @@ missing value would not be correct for these.
 The aim of the *gap filling* is now to *rescue* signal for such features by
 integrating the intensities measured within the feature's *m/z* - retention time
 area in the sample(s) in which no chromatographic peak was detected. In *xcms*
-this can be done with the `fillChromPeaks` function and the `ChromPeakAreaParam`
-to configure the gap filling. Below we perform gap filling showing also the
-number of missing values before and after running `fillChromPeaks`.
+this can be done with the `fillChromPeaks()` function and the
+`ChromPeakAreaParam` parameter to configure the gap filling. Below we perform
+gap filling showing also the number of missing values before and after running
+`fillChromPeaks()`.
 
 ```{r fillChromPeaks}
 #' Number of missing values
@@ -1727,7 +1731,7 @@ mse <- fillChromPeaks(mse, param = ChromPeakAreaParam())
 sum(is.na(featureValues(mse)))
 ```
 
-With `fillChromPeaks` we could thus *rescue* signal for all but 26
+With `fillChromPeaks()` we could thus *rescue* signal for all but 26
 features. Also for the 4 example features from above a signal was
 filled-in. Below we visualize the gap-filled chromatographic peaks for these.
 
@@ -1771,7 +1775,7 @@ QC samples or other repeatedly measured samples were no difference in feature
 abundances between samples is expected.
 
 The code below extracts first only the detected feature values (by setting
-`filled = FALSE` in the `featureValues` call), then the detected **and**
+`filled = FALSE` in the `featureValues()` call), then the detected **and**
 filled-in signal. For the latter, the detected signal is subsequently replaced
 with `NA` to create a data matrix with only filled-in values. Finally, after
 calculating the row averages for both matrices (excluding missing values), these
@@ -1835,7 +1839,7 @@ object. This includes the identified chromatographic peaks, the alignment
 results as well as the correspondence results. In addition, to guarantee
 reproducibility, this result object keeps track of all performed processing
 steps and contains also the individual parameter objects used in the various
-preprocessing steps. These can be extracted with the `processHistory`
+preprocessing steps. These can be extracted with the `processHistory()`
 function:
 
 ```{r}
@@ -1854,18 +1858,19 @@ Thus, the used preprocessing algorithms along with all their settings are
 reported along with the preprocessing results.
 
 As described above, values for the individual features can be extracted from the
-result object with the `featureValues` function and the definition of the
+result object with the `featureValues()` function and the definition of the
 features (which could be used for an initial annotation of the features based on
-their *m/z* and/or retention times) using the `featureDefinitions` function. In
-addition, the `XcmsExperiment` result object, through the internal `Spectra`
-object, keeps a *link* to the full MS data used for the analysis. For downstream
-analyses, that don't need access to this MS data anymore, the preprocessing
-results could be represented equally well using a `SummarizedExperiment` object,
-which is Bioconductor's standard container for large-scale omics data. *xcms*
-provides with the `quantify` function a convenience function to extract all
-results from an `XcmsExperiment` result object and return it as a
-`SummarizedExperiment`. This function takes the same parameters than the
-`featureValues`, which is internally used to extract the feature value matrix.
+their *m/z* and/or retention times) using the `featureDefinitions()`
+function. In addition, the `XcmsExperiment` result object, through the internal
+`Spectra` object, keeps a *link* to the full MS data used for the analysis. For
+downstream analyses, that don't need access to this MS data anymore, the
+preprocessing results could be represented equally well using a
+`SummarizedExperiment` object, which is Bioconductor's standard container for
+large-scale omics data. *xcms* provides with the `quantify()` function a
+convenience function to extract all results from an `XcmsExperiment` result
+object and return it as a `SummarizedExperiment`. This function takes the same
+parameters than `featureValues()`, which is also internally used to extract the
+feature value matrix.
 
 ```{r, warning = FALSE, message = FALSE}
 #' Extract results as a SummarizedExperiment
@@ -1873,9 +1878,9 @@ library(SummarizedExperiment)
 res <- quantify(mse, method = "sum")
 ```
 
-The sample annotations can now be accessed with the `colData` function and the
+The sample annotations can now be accessed with the `colData()` function and the
 feature definitions (i.e. annotation for individual rows/features) with the
-`rowData` function:
+`rowData()` function:
 
 ```{r}
 #' Get sample annotations
@@ -1886,7 +1891,7 @@ rowData(res)
 ```
 
 The feature values are stored as an *assay* within the object. To access that we
-simply use the `assay` function.
+simply use the `assay()` function.
 
 ```{r}
 #' Get feature values
@@ -1987,7 +1992,7 @@ serine_pks <- chromPeaks(mse, mz = serine_mz, ppm = 20)
 serine_pks
 ```
 
-The `chromPeakChromatograms` function can then be used to extract the EIC of a
+The `chromPeakChromatograms()` function can then be used to extract the EIC of a
 specific chromatographic peak in a sample.
 
 ```{r}
@@ -1996,7 +2001,7 @@ serine_eic_2 <- chromPeakChromatograms(mse, peaks = rownames(serine_pks)[2])
 ```
 
 We can also extract the full MS1 scan (spectrum) at the apex position of that
-chromatographic peak using the `chromPeakSpectra` function with parameters
+chromatographic peak using the `chromPeakSpectra()` function with parameters
 `msLevel = 1` and `method = "closest_rt"`.
 
 ```{r}
@@ -2026,17 +2031,18 @@ points(serine_mz, y = -300, pch = 2, col = "red")
 ```
 
 Similar information can also be extracted for LC-MS features using the
-`featureChromatograms` and `featureSpectra` functions but these functions will
-return chromatograms and spectra for **all** samples in the experiment (not just
-for a single sample). Also, importantly, while the `chromPeakChromatogram`
-extracts the EIC specific for the selected sample, i.e. using the exact *m/z*
-and retention time ranges of the chromatographic peak in that sample,
-`featureChrommatograms` will instead integrate the signal from the *m/z* and
-retention time area of the **feature**, i.e. will use a single area and
-integrate the signal from that same area in each sample. This *m/z* - retention
-time area might however be larger than the respective ranges for a single
-chromatographic peak in one sample. This *m/z* - retention time area for
-features can also be extracted (and evaluated) using the `featureArea` function:
+`featureChromatograms()` and `featureSpectra()` functions but these functions
+will return chromatograms and spectra for **all** samples in the experiment (not
+just for a single sample). Also, importantly, while the
+`chromPeakChromatogram()` extracts the EIC specific for the selected sample,
+i.e. using the exact *m/z* and retention time ranges of the chromatographic peak
+in that sample, `featureChrommatograms()` will instead integrate the signal from
+the *m/z* and retention time area of the **feature**, i.e. will use a single
+area and integrate the signal from that same area in each sample. This *m/z* -
+retention time area might however be larger than the respective ranges for a
+single chromatographic peak in one sample. This *m/z* - retention time area for
+features can also be extracted (and evaluated) using the `featureArea()`
+function:
 
 ```{r}
 #' Extract the m/z - retention time area for features
@@ -2078,8 +2084,8 @@ plotSpectra(serine_ms1_2, col = cols, lwd = 2)
 
 While in the example above we were specifically looking for potential isotopes
 of a single, selected, mass peak (by setting `seedMz` to the *m/z* value of that
-peak), we could also use `isotopologues` without specifying `seedMz` to identify
-all potential isotope groups in a spectrum.
+peak), we could also use `isotopologues()` without specifying `seedMz` to
+identify all potential isotope groups in a spectrum.
 
 ```{r}
 #' Identify all potential isotope peaks in the MS1 spectrum
@@ -2103,7 +2109,7 @@ Note that such isotopologue identification is not limited to data from a MS1
 spectrum. We could also identify features representing signal from
 potential isotopes. For this we below create a `matrix` with the features' *m/z*
 values and the maximum intensity of the feature in one of the samples and apply
-the `isotopologues` function on it.
+the `isotopologues()` function on it.
 
 ```{r}
 #' Define a matrix with the m/z and intensity values for each feature.
@@ -2152,7 +2158,7 @@ space from an LC-MS experiment.
 
 We below subset the data to the first sample and visualize the identified
 chromatographic peaks in the *m/z* - retention time plane using the
-`plotChromPeaks` function that we used already before.
+`plotChromPeaks()` function that we used already before.
 
 ```{r, fig.cap = "Position of identified chromatographic peaks in the first sample."}
 #' Plot identified chromatographic peaks in the first sample
@@ -2175,7 +2181,7 @@ chrs_all <- chromPeakChromatograms(mse_1, expandRt = 4)
 ```
 
 While we could now simply proceed and plot each of the `r length(chrs_all)` EICs
-separately, we instead use below the `plotChromatogramsOverlay` function that
+separately, we instead use below the `plotChromatogramsOverlay()` function that
 allows to plot multiple EICs into the same plot hence providing an overview of
 the full set of identified chromatographic peaks. By setting parameter `stacked`
 to a value different then `0` it is possible to *stack* the chromatograms along
@@ -2224,7 +2230,7 @@ chrs_sub <- chromatogram(mse_1, mz = pks[, c("mzmin", "mzmax")],
 ```
 
 We can now plot the EICs from that region again using the
-`plotChromatogramsOverlay` function. Next to plotting the data, this function
+`plotChromatogramsOverlay()` function. Next to plotting the data, this function
 also silently returns the y-positions of the individual EICs in the plot. We
 assign that below to a variable `y` and use this information to draw the *m/z*
 for the EICs along the y-axis.
@@ -2245,7 +2251,7 @@ text(x = rep(126, length(mzs)), y = y[[1]],
 ```
 
 Some of the EICs seem to represent signals from isotopes (e.g. the EIC at 114.07
-and 115.07). In fact, we can again use the `isotopologues` function from the
+and 115.07). In fact, we can again use the `isotopologues()` function from the
 `r Biocpkg("MetaboCoreUtils")` to check whether pairs of *m/z* and intensity
 values would match signal expected for isotopes.
 

From 8ea06203de3f7acb4f906c5ad5f518ffa60f23a7 Mon Sep 17 00:00:00 2001
From: Johannes Rainer <johannes.rainer@gmail.com>
Date: Wed, 7 Aug 2024 11:36:15 +0200
Subject: [PATCH 41/42] Update README and add CITATION file

---
 DESCRIPTION   |  2 +-
 NEWS.md       | 11 ++++++++---
 README.md     | 10 ++++++++++
 inst/CITATION | 19 +++++++++++++++++++
 4 files changed, 38 insertions(+), 4 deletions(-)
 create mode 100644 inst/CITATION

diff --git a/DESCRIPTION b/DESCRIPTION
index afd45d2..1e268a3 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: xcmsTutorials
 Title: Exploring and Analyzing LC-MS data with Spectra and xcms
-Version: 1.1.1
+Version: 1.1.2
 Authors@R: c(
 	     person(given = "Johannes", family = "Rainer",
 		    email = "Johannes.Rainer@eurac.edu",
diff --git a/NEWS.md b/NEWS.md
index d8ef24d..3325409 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,4 +1,8 @@
-# xcmsTutorials 1.0
+# xcmsTutorials 1.1
+
+## Changes in 1.1.2
+
+- Add citation information to the README and the package.
 
 ## Changes in 1.1.1
 
@@ -9,7 +13,7 @@
 
 ## Changes in 1.1.0
 
-- Release the tutorial to Zenodo.
+- Release the tutorial to Zenodo: https://doi.org/10.5281/zenodo.11185521
 - Use Bioconductor 3.19 release docker image and packages.
 - Add examples for simple quality assessment of BPC or BPS data.
 - Introduce parameter `ppm` for `PeakDensityParam` correspondence analysis
@@ -17,6 +21,8 @@
 - Add an additional section for extraction of chromatographic or spectra data
   for identified chromatographic peaks and perform isotopologue search in these.
 
+# xcmsTutorials 1.0
+
 ## Changes in 1.0.4
 
 - Use the Bioconductor 3.18 release docker image.
@@ -52,7 +58,6 @@
 
 - Add requirements for `xcms` and `Spectra` packages as well as R version.
 
-
 # xcmsTutorials 0.2
 
 ## Changes in xcmsTutorials 0.2.0
diff --git a/README.md b/README.md
index 8cb06a8..da1b301 100644
--- a/README.md
+++ b/README.md
@@ -46,6 +46,16 @@ generate the html file
 [xcms-preprocessing.html](https://jorainer.github.io/xcmsTutorials/xcms-preprocessing.html).
 
 
+## Citation
+
+This workflow (and future versions) are deposited at
+[Zenodo](https://zenodo.org). To cite this version of the tutorial:
+
+Johannes Rainer and Philippine Louil. xcmsTutorials version 1.1.0: Exploring and
+analyzing LC-MS data with Spectra and xcms. Zenodo, May
+13, 2024. https://doi.org/10.5281/zenodo.11185521.
+
+
 ## Installation
 
 The workshop files along with an R runtime environment including all required
diff --git a/inst/CITATION b/inst/CITATION
new file mode 100644
index 0000000..902e73c
--- /dev/null
+++ b/inst/CITATION
@@ -0,0 +1,19 @@
+citHeader("To cite this workflow:")
+
+citEntry(entry = "Manual",
+         title  = "xcmsTutorials: Exploring and analyzing LC-MS data with Spectra and xcms",
+         author = personList(
+           person(given = "Johannes", family = "Rainer"),
+           person(given = "Philippine", family = "Louail")
+         ),
+         publisher = "Zenodo",
+         year = "2024",
+         month = "may",
+         version = "v1.1.0",
+         doi = "10.5281/zenodo.11185521",
+         url = "https://doi.org/10.5281/zenodo.11185521",
+         textVersion = paste0("Johannes Rainer, & Philippine Louail. (2024). ",
+                              "xcmsTutorials version 1.1.0: Exploring and ",
+                              "analyzing LC-MS data with Spectra and xcms. ",
+                              "Zenodo. https://doi.org/10.5281/zenodo.11185521")
+         )

From 69e2f98f22e43de70d3248e28ec3f1067bf0d76d Mon Sep 17 00:00:00 2001
From: Johannes Rainer <johannes.rainer@gmail.com>
Date: Wed, 7 Aug 2024 13:00:44 +0200
Subject: [PATCH 42/42] Fix name

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index da1b301..691e95a 100644
--- a/README.md
+++ b/README.md
@@ -51,8 +51,8 @@ generate the html file
 This workflow (and future versions) are deposited at
 [Zenodo](https://zenodo.org). To cite this version of the tutorial:
 
-Johannes Rainer and Philippine Louil. xcmsTutorials version 1.1.0: Exploring and
-analyzing LC-MS data with Spectra and xcms. Zenodo, May
+Johannes Rainer and Philippine Louail. xcmsTutorials version 1.1.0: Exploring
+and analyzing LC-MS data with Spectra and xcms. Zenodo, May
 13, 2024. https://doi.org/10.5281/zenodo.11185521.