We use Miniconda (or Anaconda) for environment management (See environment.yml file). To create the environment: conda env create -f environment.yml

We use R version 4.1.2 and Bioconductor version 3.14.

All R packages are installed through conda.

Clone this repository to your local machine and open the project through mdd.Rproj.

This project is organized in the following directories:

• data/: Holds the RNA-seq processed data, study metadata, and genome references;

  • genome/: auxiliary files for kallisto quantification.
    • Homo_sapiens.GRCh38.97.gtf.gz: GTF file from human gencode/Ensembl version 97.
    • Homo_sapiens.GRCh38.cdna.all.fa.gz: Human transcriptome gencode/Ensembl version 97.
  • kallisto/: output from kallisto quantification mode (abundance.tsv). Quantification files are separated by brain region.
    • aINS/:
    • Cg25/:
    • dlPFC/:
    • OFC/:
    • Nac/:
    • Sub/:
  • meta/: metadata for BioProject SRP115956.
    • SraRunTable.txt : study metadata;
    • Metadata.csv : Information on individual samples. This information was previously available here: http://neuroscience.mssm.edu/nestler/contecenter/listchromatingenedatabase.html.
  • vcfs: VCF files filtered by the variants showed in Table 1 from the paper.

• scripts/: Holds all the scripts used in the analyses;

• results/: Holds results from each step analysis.

• Quantification

Script for download, processing and quantification by kallisto;

• Exploratory analysis:

  • Metadata:
    • metadata: Organizes metadata for further steps.
  • Transcript and gene estimates:
    • tx_gene: Uses tximport to summarise gene counts and prepare data for further steps.
    • tx_tx: Uses tximport to prepare data to further steps.
  • Outlier identification:
    • robust_pca: Performs robust PCA analysis of samples using rrcov;
    • remove_outliers_samples: Removes the outliers samples chosen by Robust PCA analysis.
  • Covariates selection:
    • impute_meta: Imputes data for missing values found in some metadata covariates.
    • rank_variables: Performs covariate analysis.

• TAG:

  • Feature-wise outlier detection:
    • outliers_edge_ppcseq_gene: Performs identification of outlier genes by ppcseq. Outlier genes were removed from DGE analysis.
    • outliers_edge_ppcseq_tx: Performs identification of outlier transcripts by ppcseq. Outlier transcripts were removed from DTE and DTU analyses.
  • Differential gene expression:
    • edger_diff_gene: Differential gene expression with edgeR.
  • Differential transcript expression:
    • edger_diff_tx: Differential transcript expression with edgeR.
    • diff_tx_correct: Performs multiple hypothesis correction with stageR.
  • Differential transcript usage:
    • ISA/: scripts for differential transcript usage using IsoformSwitchAnalyzeR are stored in this directory.
  • Gather results from three methods:
    • organize_dge_dte_after_filtering: Filters the outlier genes and transcripts identified by ppcseq from DGE and DTE results.
    • summarise_results_dge_dte_dtu: Removes outlier transcripts from DTU analysis and gathers results from three methods.

• Functional analyses:

  • network: Network inference using stringDB. Visualization by RedeR and ggraph.
  • enrichment: Enrichment analysis of transcriptionally altered genes using clusterProfiler.
  • gwas_intersections: Get genes with genomic regions related to depression using gwasrapidd.
  • intersection_analysis: intersection analysis by sex, brain region, and method used.

• Additional scripts:

  • plots and plot_dtu: Description of figures produced to the paper.
  • supp_fig_variants_by_donors: Description of Supplementary Figures 9 and 10, which represent the presence of depression-associated SNPs on the samples considered.