Bulk RNA-seq DESeq2 analysis with omicverse

What problem does it solves? Conducting robust differential expression analysis for bulk RNA-seq data using DESeq2 involves several steps, from gene ID mapping to statistical testing and visualization, which can be intricate. This Skill automates the PyDESeq2 workflow within omicverse, simplifying the entire process.

Core Features & Use Cases

• Gene ID Mapping: Convert gene identifiers (e.g., Ensembl IDs) to gene symbols for consistent analysis.
• DESeq2 Differential Expression: Perform DESeq2-based differential expression testing between treatment and control groups.
• Visualization & Filtering: Filter results by fold-change and p-value, and visualize with volcano plots and boxplots.
• Gene Set Enrichment Analysis (GSEA): Conduct GSEA on ranked genes to identify enriched biological pathways.
• Use Case: Analyze a bulk RNA-seq experiment comparing drug-treated vs. untreated samples, identify genes differentially expressed using DESeq2, visualize the results, and then perform GSEA to find enriched biological pathways.

Quick Start

Run DESeq2 differential expression on my bulk RNA-seq counts, generate a volcano plot, and perform GSEA on WikiPathways.