microbiome · TuomasBorman · Sep 26, 2025 · Sep 15, 2025 · Sep 15, 2025 · Sep 15, 2025
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: OMA
 Title: Orchestrating Microbiome Analysis with Bioconductor
-Version: 0.98.47
-Date: 2025-09-11
+Version: 0.98.48
+Date: 2025-09-26
 Authors@R:
     c(
   person(given = "Tuomas", family = "Borman", role = c("aut", "cre"), email = "[email protected]", comment = c(ORCID = "0000-0002-8563-8884")),
@@ -30,6 +30,7 @@ Suggests:
     BiocBook,
     BiocManager,
     BiocParallel,
+    BiocStyle,
     Biostrings,
     bluster,
     caret,

diff --git a/inst/pages/agglomeration.qmd b/inst/pages/agglomeration.qmd
@@ -54,8 +54,8 @@ tse <- GlobalPatterns
 One of the main applications of taxonomic information in sequencing data
 is to agglomerate counts (such as ASV counts) to specific taxonomic levels
 and to model how different sample-specific variables may influence the feature
-composition at different taxonomic levels. For this, `mia` contains the
-`agglomerateByRank()` function.
+composition at different taxonomic levels. For this,
+`r BiocStyle::Biocpkg("mia")` contains the `agglomerateByRank()` function.
 
 At its simplest, the function takes a `TreeSE` object as input and outputs a
 `TreeSE` object agglomerated to a specified taxonomy level using the `rank`

diff --git a/inst/pages/alpha_diversity.qmd b/inst/pages/alpha_diversity.qmd
@@ -129,9 +129,9 @@ modern 16S data, which commonly features denoising and removal of singletons
 
 Alpha diversity can be estimated with the `addAlpha()` function, which includes
 built-in methods for calculating some indices, while others are computed via
-integration with the `vegan` [@R_vegan] package. The method calculates the
-given indices, and add them to the `colData` slot of the `SummarizedExperiment`
-object with the given `name`.
+integration with the `r BiocStyle::CRANpkg("vegan")` package [@R_vegan].
+The method calculates the given indices, and add them to the `colData` slot
+of the `SummarizedExperiment` object with the given `name`.
 
 ```{r}
 #| label: calc_diversity
@@ -173,7 +173,8 @@ used by default. However, the optional argument `tree` must be provided if
 As alpha diversity metrics typically summarize high-dimensional samples into
 singular values, many visualization approaches are available. Once calculated,
 these metrics can be analyzed directly from the `colData`, for example, by
-plotting them using `plotColData()` from the `scater` package [@R_scater].
+plotting them using `plotColData()` from the `r BiocStyle::Biocpkg("scater")`
+package [@R_scater].
 Here, we use the `observed` species as a measure of richness. Let's visualize
 the results against selected `colData` variables (sample type and final
 barcode).
@@ -303,7 +304,8 @@ pairwise.wilcox.test(
 
 Next, let's compare the Shannon index between sample groups and visualize the
 statistical significance. Using the `stat_compare_means` function from the
-`ggpubr` package, we can add visually appealing p-values to our plots.
+`r BiocStyle::CRANpkg("ggpubr")` package, we can add visually appealing
+p-values to our plots.
 
 To add adjusted p-values, we first have to calculate them.
 
@@ -373,7 +375,7 @@ p
 
 ## Further reading
 
-An article on [`ggpubr` package](http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/76-add-p-values-and-significance-levels-to-ggplots/)
+An article on [ggpubr package](http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/76-add-p-values-and-significance-levels-to-ggplots/)
 provides further examples for estimating and highlighting significances.
 
 ::: {.callout-tip icon="false"}

diff --git a/inst/pages/clustering.qmd b/inst/pages/clustering.qmd
@@ -33,10 +33,10 @@ form the clusters based on the dissimilarity matrix. The data can be clustered
 either based on features or on samples. The examples below are focused on
 sample clustering.
 
-There are multiple clustering algorithms available. `bluster` is a
-Bioconductor package providing tools for clustering data in the
-`SummarizedExperiment` container. It offers multiple algorithms such as
-hierarchical clustering, DBSCAN, and K-means.
+There are multiple clustering algorithms available.
+`r BiocStyle::Biocpkg("bluster")` is a Bioconductor package providing tools
+for clustering data in the `SummarizedExperiment` container. It offers multiple
+algorithms such as hierarchical clustering, DBSCAN, and K-means.
 
 ```{r}
 #| label: load_bluster
@@ -77,9 +77,10 @@ that contains all observations. Clusters are split recursively into clusters
 that differ the most. The clustering can be continued until each cluster
 contains only one observation.
 
-In this example we use the `addCluster()` function from `mia` to cluster the
-data. The `addCluster()` function allows to choose a clustering algorithm and
-offers multiple parameters to shape the result. The `HclustParam()` parameter
+In this example we use the `addCluster()` function from
+`r BiocStyle::Biocpkg("mia")` to cluster the data. The `addCluster()`
+function allows to choose a clustering algorithm and offers multiple
+parameters to shape the result. The `HclustParam()` parameter
 is used for hierarchical clustering, and it accepts several sub-parameters, as
 further detailed in [HclustParam documentation](https://rdrr.io/github/LTLA/bluster/man/HclustParam-class.html).
 Notably, the `by` argument specifies whether clustering is performed based on
@@ -348,11 +349,12 @@ community type and *W* representing continuous sample memberships across all
 community types. The NMF algorithm calculates the minimum distance between the
 original data and its approximation using Kullback-Leibler divergence.
 
-Here, we use the `mia` wrapper `getNMF`. To reduce calculation time, we set the
-number of components `k` to four instead of optimizing the number of components
-that describe the original data the best. The matrix returned by `getNMF`
-contains the sample scores, while the NMF output and NMF feature loadings are
-stored in the attributes `NMF_output` and `loadings`, respectively.
+Here, we use the `r BiocStyle::Biocpkg("mia")` wrapper `getNMF`. To reduce
+calculation time, we set the number of components `k` to four instead of
+optimizing the number of components that describe the original data the best.
+The matrix returned by `getNMF` contains the sample scores, while the NMF
+output and NMF feature loadings are stored in the attributes `NMF_output`
+and `loadings`, respectively.
 
 ```{r}
 #| label: nmf1

diff --git a/inst/pages/community_similarity.qmd b/inst/pages/community_similarity.qmd
@@ -124,12 +124,13 @@ tse$Group <- tse$SampleType == "Feces"
 
 The standard Jaccard index is calculated based on a presence/absence table.
 However, a common mistake is using the quantitative version instead. By
-default, `vegan::vegdist()` computes the abundance-weighted Jaccard
+default, the function `vegdist()` from the `r BiocStyle::CRANpkg("vegan")` 
+package computes the abundance-weighted Jaccard
 dissimilarity, which is similar to Bray-Curtis. This can be controlled using
 the `binary` parameter.
 
-When calculating the Jaccard index with `mia`'s functions, it defaults to
-standard presence/absence version:
+When calculating the Jaccard index with `r BiocStyle::Biocpkg("mia")`'s
+functions, it defaults to standard presence/absence version:
 
 ```
 tse <- addDissimilarity(tse, assay.type = "counts", method = "jaccard")
@@ -273,7 +274,8 @@ and visualize the results.
 
 In the following examples dissimilarity is calculated with the function
 supplied to the `FUN` argument. Several metrics of beta diversity are defined
-by the `vegdist()` function of the `vegan` package, which is often used in
+by the `vegdist()` function of the `r BiocStyle::CRANpkg("vegan")`
+package, which is often used in
 this context. However, custom functions created by the user also work, as long
 as they return a `dist` object. In either case, this function is then applied
 to calculate reduced dimensions via an ordination method, the results of which
@@ -297,7 +299,8 @@ tse <- addMDS(
 ```
 
 Sample dissimilarity can be visualized on a lower-dimensional display
-(typically 2D) using the `plotReducedDim()` function from the `scater` package.
+(typically 2D) using the `plotReducedDim()` function from the
+`r BiocStyle::Biocpkg("scater")` package.
 This also provides tools to incorporate additional information encoded by
 color, shape, size and other aesthetics. Can you find any difference between
 the groups?
@@ -375,9 +378,9 @@ tse <- addNMDS(
 ```
 
 Multiple ordination plots are combined into a multi-panel plot with the
-`patchwork` package, so that different methods can be compared to find
-similarities between them or select the most suitable one to visualize
-beta diversity in the light of the research question.
+`r BiocStyle::CRANpkg("patchwork")` package, so that different methods 
+can be compared to find similarities between them or select the most
+suitable one to visualize beta diversity in the light of the research question.
 
 ```{r}
 #| label: mds-nmds_comparison2
@@ -570,15 +573,15 @@ p <- lapply(1:2, function(i) {
 wrap_plots(p)
 ```
 
-The `mia` package's `addAlpha()` and `getDissimilarity()` functions support
-rarefaction in alpha and beta diversity calculations. Additionally, the
-`rarefyAssay()` function allows random subsampling of a given assay within a
-`TreeSummarizedExperiment` dataset.
+The `r BiocStyle::Biocpkg("mia")` package's `addAlpha()` and
+`getDissimilarity()` functions support rarefaction in alpha and beta diversity
+calculations. Additionally, the `rarefyAssay()` function allows random
+subsampling of a given assay within a `TreeSummarizedExperiment` dataset.
 
 ### Other ordination methods {#sec-other-ord-methods}
 
 Other dimension reduction methods, such as PCA and UMAP, are inherited from the
-`scater` package.
+`r BiocStyle::Biocpkg("scater")` package.
 
 ```{r}
 #| label: plot_pca
@@ -760,9 +763,8 @@ groups. A p-value smaller than the significance threshold indicates that the
 groups have a different community composition.
 
 This method is implemented with the
-[`adonis2`](https://www.rdocumentation.org/packages/vegan/versions/2.4-2/topics/adonis)
-function from the `vegan` package. You can find more on PERMANOVA from
-[here](https://microbiome.github.io/OMA/docs/devel/pages/97_extra_materials.html/#compare-permanova).
+[`adonis2`](https://www.rdocumentation.org/packages/vegan/versions/2.7-1/topics/adonis)
+function from the `r BiocStyle::CRANpkg("vegan")` package. 
 
 From the results table below, we see that both clinical status and age explain
 more than 10% of the variance, however, only age has statistical significance.
@@ -776,7 +778,7 @@ rda_info$permanova |>
 
 Next, we proceed to visualize the weight and significance of each variable on
 the similarity between samples with an RDA plot, which can be generated with
-the `plotRDA()` function from the `miaViz` package.
+the `plotRDA()` function from the `r BiocStyle::Biocpkg("miaViz")` package.
 
 ```{r}
 #| label: plot_rda
@@ -798,9 +800,9 @@ complements the previous results obtained with PERMANOVA.
 Calculating dissimilarities between samples is a computationally demanding
 task, which can make methods like dbRDA or PCoA time-consuming with large
 datasets. To speed up calculations, consider using functions that support
-parallel processing instead of the default `vegan` package options. Packages
-like `parallelDist` can significantly improve performance on systems with
-multiple available cores.
+parallel processing instead of the default `r BiocStyle::CRANpkg("vegan")`
+package options. Packages like `r BiocStyle::CRANpkg("parallelDist")`
+can significantly improve performance on systems with multiple available cores.
 
 In `*Dissimilarity()` functions, you can specify the utilized dissimilarity
 function with `dis.fun` argument.
@@ -1048,10 +1050,11 @@ plotReducedDim(
 ## Summary
 
 As a final note, we provide a comprehensive list of functions for the
-evaluation of dissimilarity indices available in the `mia` and `scater`
-packages. The `calculate` methods return a reducedDim object as an output,
-whereas the `run` methods store the reducedDim object into the specified
-TreeSE.
+evaluation of dissimilarity indices available in the
+`r BiocStyle::Biocpkg("mia")` and `r BiocStyle::Biocpkg("scater")`
+packages. The `calculate` methods return a `reducedDim` object as an output,
+whereas the `run` methods store the `reducedDim` object into the specified
+`TreeSE`.
 
 * Canonical Correspondence Analysis (CCA): `getCCA()` and `runCCA()`
 * dbRDA: `getRDA()` and `runRDA(0)`; our recommended default method to assess

diff --git a/inst/pages/composition.qmd b/inst/pages/composition.qmd
@@ -89,9 +89,9 @@ tse <- transformAssay(
 )
 ```
 
-We can visualize the heatmap with the
-[`*sechm*`](http://www.bioconductor.org/packages/release/bioc/vignettes/sechm/inst/doc/sechm.html)
-package. It is a wrapper for the *ComplexHeatmap* package [@ComplexHeatmap].
+We can visualize the heatmap with the `r BiocStyle::Biocpkg("sechm")`
+package. It is a wrapper for the `r BiocStyle::Biocpkg("ComplexHeatmap")`
+package [@ComplexHeatmap].
 
 ```{r}
 #| label: heatmap
@@ -111,8 +111,8 @@ heatmap
 ```
 
 Another method to visualize community composition is by plotting a
-NeatMap, which employs radial theta sorting when plotting the
-heatmap [@Rajaram2010]. The `getNeatOrder()` function in the `miaViz`
+NeatMap, which employs radial theta sorting when plotting the heatmap
+[@Rajaram2010]. The `getNeatOrder()` function in the `r BiocStyle::Biocpkg("miaViz")`
 package allows us to achieve this. This method sorts data points based
 on their angular position in a 2D space, typically after an ordination
 technique such as PCA or NMDS has been applied. The `getNeatOrder()` method
@@ -123,7 +123,7 @@ between data points according to the ordination method's spatial
 configuration, rather than relying on hierarchical clustering.
 
 Now, we'll perform the aforementioned steps to create a NeatMap using the
-`sechm` package and the `getNeatOrder()` function.
+`r BiocStyle::Biocpkg("sechm")` package and the `getNeatOrder()` function.
 
 ```{r}
 #| label: neatmap
@@ -157,11 +157,11 @@ neatmap
 ::: callout-tip
 ## Additional heatmap visualization
 
-In addition, there are also other packages that provide functions for
-more complex heatmaps, such as those provided by
-[*iheatmapr*](https://docs.ropensci.org/iheatmapr/articles/full_vignettes/iheatmapr.html)
-and *ComplexHeatmap* [@ComplexHeatmap]. The utilization of
-`ComplexHeatmap` for clustered heatmaps is explained in [@sec-clustered-heatmap].
+In addition, there are also other packages that provide functions for more
+complex heatmaps, such as those provided by `r BiocStyle::CRANpkg("iheatmapr")`
+and `r BiocStyle::Biocpkg("ComplexHeatmap")` [@ComplexHeatmap]. The utilization
+of `r BiocStyle::Biocpkg("ComplexHeatmap")` for clustered heatmaps is explained
+in [@sec-clustered-heatmap].
 :::
 
 ::: {.callout-tip icon="false"}