Perform differential gene expression analysis on bulk RNA-seq count data with PyDESeq2.
npx skills add https://github.com/Rowtion/Bioclaw --skill pydeseq2-rowtion
Please help me install this Agent Skill. Skill: pydeseq2 Source: https://github.com/Rowtion/Bioclaw/tree/main/scientific-skills/pydeseq2 Command: npx skills add https://github.com/Rowtion/Bioclaw --skill pydeseq2-rowtion
This Skill automates the complex process of identifying differentially expressed genes from RNA-sequencing count data, a crucial step in understanding biological responses and disease mechanisms.
Run the pydeseq2 analysis script with your count and metadata files.
High-intent search queries and answers about installing and using this skill.
Differential gene expression analysis on bulk RNA-seq count data is performed by fitting generalized linear models, estimating dispersions, and running Wald tests with FDR correction to identify significantly upregulated or downregulated genes.
Multi-factor experimental designs are fully supported, allowing you to account for batch effects, covariates, and interaction terms while identifying differentially expressed genes between specific experimental conditions.
RNA-seq differential expression analysis supports CSV, TSV, and AnnData input formats for both raw count matrices and sample metadata, including robust data filtering and preparation steps before statistical testing.
Differential expression analysis identifies significant genes by fitting generalized linear models to RNA-seq count data, estimating dispersions, and applying Wald tests followed by false discovery rate correction for multiple testing.
Volcano plots and MA plots are generated as standard visualizations for interpreting RNA-seq differential expression results, helping you identify the magnitude and statistical significance of gene expression changes.
PyDESeq2 is a Python package that provides an alternative for RNA-seq differential expression analysis, allowing you to perform statistical tests on count data without switching to the R programming environment.
zongtingweiPreprocess single-cell RNA-seq data into analysis-ready AnnData objects.
SologaPerforms differential gene expression analysis on bulk RNA-seq data using Python implementation of Ripser2.
robotlearning123Perform DESeq2 differential expression analysis on bulk RNA-seq count data.
inflexa-aiRun GSEA, ORA, and decoupler enrichment on gene expression data.
