Skip to content

Latest commit

 

History

History
96 lines (63 loc) · 4.32 KB

README.md

File metadata and controls

96 lines (63 loc) · 4.32 KB
Raw

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.

All R packages are installed through conda.

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

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.