Bulk RNA-seq differential expression with omicverse

What problem does it solves? Analyzing bulk RNA-seq data for differentially expressed genes (DEGs) involves multiple steps from raw counts to statistical testing and pathway enrichment, which can be complex and time-consuming. This Skill automates the entire pipeline, providing a streamlined workflow for robust DEG analysis.

Core Features & Use Cases

• Gene ID Mapping: Convert gene identifiers (e.g., Ensembl IDs) to gene symbols for consistent analysis.
• Differential Expression Testing: Perform DESeq2-style normalization and statistical testing (t-test, edgeR, limma).
• Visualization & Filtering: Generate volcano plots and boxplots, with dynamic fold-change and significance thresholding.
• Pathway Enrichment: Conduct comprehensive pathway enrichment analysis on significant DEGs to uncover biological insights.
• Use Case: Analyze a bulk RNA-seq dataset comparing treated vs. control samples, identify significant DEGs, visualize them with volcano plots, and then perform GO pathway enrichment to understand affected biological processes.

Quick Start

Run differential expression analysis on my bulk RNA-seq counts using a t-test, then generate a volcano plot and perform WikiPathways enrichment.