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Repository for scripts used in Gene- and transcript-level analyses reveal sex-specific trancriptional alterations in prefrontal cortex in Major Depressive Disorder.

Requisites

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.

Here, we use multiple R packages. We use renv to track R package versions (see renv.lock file).

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

Overall project structure

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.
    • 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.

Description of scripts used

  • 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.