bulk-rna-seq-differential-expression-with-omicverse

Bulk RNA-seq differential expression analysis processes featureCounts-style gene-level count matrices to identify and rank differentially expressed genes. The workflow includes ID mapping, normalization, statistical testing, visualization, and pathway enrichment.

Yes, gene ID mapping converts Ensembl or other gene identifiers to gene symbols during the differential expression workflow. It supports downloadable mapping pairs and custom GTF-derived mappings for accurate identifier conversion.

Bulk RNA-seq differential expression testing supports Welch t-test, edgeR-like testing, and limma-style modeling. It applies DESeq2-style size factor normalization prior to statistical testing to ensure accurate gene ranking.

Volcano plots, per-gene boxplots, and enriched pathway visualizations are generated automatically after differential expression testing. The workflow also prepares genesets for multiple organisms and plots single and combined ontologies.

Yes, the workflow is designed for treatment versus control cohort comparisons on bulk RNA-seq data. It includes defensive checks for group membership, duplicate handling, and filtering recommendations to validate cohort assignments.

DESeq2-style size factor normalization is suited for gene-level count matrices from bulk RNA-seq experiments. It requires featureCounts-style inputs and performs best with validated treatment versus control group memberships and proper duplicate handling.