Perform differential gene expression analysis on bulk RNA-seq count data.
npx skills add https://github.com/sanand0/scientific-research --skill pydeseq2-sanand0
Please help me install this Agent Skill. Skill: pydeseq2 Source: https://github.com/sanand0/scientific-research/tree/main/.claude/skills/pydeseq2 Command: npx skills add https://github.com/sanand0/scientific-research --skill pydeseq2-sanand0
This Skill automates the complex process of identifying differentially expressed genes from bulk RNA-sequencing data, a critical step in understanding biological responses to treatments or conditions.
Run a standard differential expression analysis on your RNA-seq counts and metadata.
High-intent search queries and answers about installing and using this skill.
Differential gene expression analysis on bulk RNA-seq count data is performed using a Python implementation of DESeq2, applying Wald tests with FDR correction to identify significantly up- or down-regulated genes.
Yes, the analysis integrates with pandas and AnnData for data handling, allowing you to input bulk RNA-seq count data and metadata directly for differential gene expression analysis.
Yes, the Python implementation of DESeq2 supports both single-factor and multi-factor experimental designs to analyze differential gene expression across various treatment conditions and metadata variables.
Differentially expressed genes are identified using Wald tests with FDR correction, and optional LFC shrinkage is provided for visualization of results from bulk RNA-seq count data.
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