Differential Expression Analysis (DESeq2) & RNA-seq Processing Pipeline Across Soft Tissue Sarcoma Groups

Overview

This repo contains scripts for preprocessing and DESeq2 analysis of sarcoma RNA-seq data.
Core flow: raw counts → filtered counts → DESeq2 inputs → analysis outputs.

Organization

• data/ → input and output files
  • counts_filtered.csv → gene x sample matrix after QC filtering
  • sarcoma_counts.rds → DESeq2-ready DESeqDataSet object
  • sarcoma_colData.csv → sample metadata (design/condition/type)
  • sarcoma_rowData.csv → gene annotations
• scripts/ → R scripts for filtering, sanity matrices, DESeq2 setup, analysis
• results/ → DESeq2 outputs, plots, differential expression tables
• renv/ → environment snapshot for reproducibility

Key Artifacts

• counts_filtered.csv → raw count matrix (genes x samples) after QC filtering.
• sarcoma_counts.rds → DESeq2-ready DESeqDataSet object.
• sarcoma_colData.csv → sample metadata (design, condition, type).
• sarcoma_rowData.csv → gene annotations linked to counts.

Workflow

1. Filtering: remove low-expression genes, save counts_filtered.csv.
2. Alignment sanity checks: verify count matrix integrity.
3. DESeq2 Prep: build sarcoma_counts.rds with matching colData + rowData.
4. Analysis: run DESeq2 differential expression, diagnostics, downstream plots.

Environment

Use renv to restore exact R package versions:

renv::restore()