Deseq2 | Skills Pool

스킬 파일

Deseq2

Differential expression analysis using DESeq2 (R/Bioconductor) via subprocess. Input: --counts <counts.csv> --metadata <metadata.csv> --contrast <treatment vs control> --output <dir>. Output: deseq2_results.csv (log2FC, padj), MA_plot.png, volcano_plot.png.

garrisonfaridi0 스타2026. 4. 7.

직업
카테고리: 과학 컴퓨팅

스킬 내용

deseq2 — Differential Expression Analysis

Run DESeq2 on a raw count matrix to identify differentially expressed genes between conditions.

Capabilities

Task	Input	Output
Two-group DE	counts.csv + metadata.csv	deseq2_results.csv, MA_plot.png
Multi-factor	counts.csv + metadata.csv (multiple cols)	deseq2_results.csv per contrast
Shrinkage	lfcShrink via apeglm	shrunken_results.csv

Requirements

R ≥ 4.3 with DESeq2, apeglm, ggplot2 installed
Input: raw integer counts (not normalized TPM/FPKM)

When to use

Bulk RNA-seq differential expression
ATAC-seq peak differential accessibility (with raw counts)
Spatial transcriptomics DE between regions

관련 스킬

python3 run_deseq2.py \
  --counts counts_matrix.csv \
  --metadata sample_metadata.csv \
  --contrast "treatment:drug_vs_control" \
  --output results/deseq2/