Differential expression analysis for bulk RNA-seq and pseudo-bulk count matrices with QC, PCA, and contrast testing.
This skill performs differential expression on bulk RNA-seq or pseudo-bulk count matrices.
.csv or .tsv): rows are genes, columns are samples, first column is gene identifier.csv or .tsv): one row per sample, must include sample_id~ condition or ~ batch + conditionfactor,numerator,denominator (e.g. )condition,treated,controlrnaseq_de_report/
├── report.md
├── figures/
│ ├── pca.png
│ ├── volcano.png
│ └── ma_plot.png
├── tables/
│ ├── qc_summary.csv
│ ├── normalized_counts.csv
│ └── de_results.csv
└── reproducibility/
├── commands.sh
├── environment.yml
└── checksums.sha256
python rnaseq_de.py \
--counts counts.csv \
--metadata metadata.csv \
--formula "~ batch + condition" \
--contrast "condition,treated,control" \
--output report_dir