Bulk RNA-seq differential expression with omicverse

Differential expression analysis identifies genes with significant expression changes between sample groups. This Skill automates the complete workflow: normalize raw counts, apply statistical testing (t-test, edgeR, limma), filter results by fold-change and p-value thresholds, and generate volcano plots—all within omicverse's integrated pipeline across multiple reference genomes.

Yes. This Skill converts gene identifiers (e.g., Ensembl IDs to gene symbols) and performs DESeq2-style normalization and statistical testing in one streamlined process. You provide raw counts, sample labels, and treatment/control groupings; the workflow handles mapping, normalization, and hypothesis testing automatically.

The Skill generates volcano plots and boxplots with dynamic fold-change and significance thresholds, allowing you to filter and visualize differentially expressed genes interactively. Visualization helps identify and communicate significant DEGs alongside statistical results.

Yes. After identifying significant DEGs, the Skill performs comprehensive pathway enrichment analysis using gseapy, supporting WikiPathways and other pathway libraries. This reveals affected biological processes and functional categories beyond individual gene results.

You need a Python environment with omicverse, scanpy, matplotlib, pandas, and gseapy installed; a raw gene-count matrix; gene ID annotation pairs in genesets/; and sample metadata with treatment versus control labels. Optional: pathway libraries for enrichment analysis.

Yes. The Skill is applicable to users with raw gene-count matrices and supports gene ID mapping across multiple reference genomes. Provide your counts, sample labels, and treatment/control groupings; the workflow produces DEG results tables, plots, and enrichment summaries for each genome context.