bulk-rna-seq-deseq2-analysis-with-omicverse

Bulk RNA-seq DESeq2 analysis is automated by importing your count matrix, mapping Ensembl IDs to gene symbols, and running DEG testing with PyDESeq2 to generate filtered results and visualizations.

Ensembl ID mapping to gene symbols is supported natively during the bulk RNA-seq workflow, preparing formatted count data for accurate DEG testing and downstream visualization.

Multiple conditions and replicates are supported for bulk RNA-seq DESeq2 analysis, applying defensive input validation to ensure robust differential expression testing across complex experimental designs.

Volcano plots and per-gene boxplots are generated to visualize DESeq2 differential expression results, with optional enrichment analysis plots available for downstream biological interpretation.

Enrichment analysis is an optional step following DEG identification in the bulk RNA-seq workflow, allowing you to interpret biological significance directly from the tested gene lists.

Defensive validation of input counts data handles common edge cases automatically during formatting, ensuring robust DESeq2 testing results without manual preprocessing errors.