Perform differential gene expression analysis on bulk RNA-seq count data with PyDESeq2.
npx skills add https://github.com/Yezez9/Research-Agent --skill pydeseq2-yezez9
Please help me install this Agent Skill. Skill: pydeseq2 Source: https://github.com/Yezez9/Research-Agent/tree/main/scientific-skills/pydeseq2 Command: npx skills add https://github.com/Yezez9/Research-Agent --skill pydeseq2-yezez9
This Skill automates the complex process of identifying differentially expressed genes from bulk RNA-sequencing count data, a fundamental task in biological research.
Run the pydeseq2 analysis script with your counts, metadata, design, and contrast.
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 the PyDESeq2 library to identify significantly expressed genes between experimental conditions via Wald tests and FDR correction. You provide count matrices, metadata, and experimental design to run the analysis.
Yes, you can use this for RNA-seq experimental designs with batch effects and multiple factors. The analysis supports single-factor and multi-factor experimental designs, including batch effect correction and interaction terms.
The best way to visualize differentially expressed genes from RNA-seq data is by generating volcano and MA plots. These visualizations help you clearly map gene expression changes and statistical significance across different experimental conditions.
FDR correction works by adjusting p-values from Wald tests to control the false discovery rate when identifying differentially expressed genes. This statistical process ensures you accurately flag genes with significant expression changes between conditions.
To run a PyDESeq2 analysis script, you need to provide bulk RNA-seq count data, metadata, an experimental design formula, and a contrast. The script uses these inputs to perform statistical testing and generate visualizations.
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