pydeseq2
Category: life-sciences · License: MIT · Version: 1.4 · Includes scripts
Tags: differential expression, RNA-seq, DESeq2, transcriptomics, bioinformatics
When to use it
Runs differential expression analysis on bulk RNA-seq count data with PyDESeq2, the Python port of DESeq2. Covers formulaic single- and multi-factor designs, contrasts, Wald tests, Benjamini-Hochberg FDR correction, optional apeGLM LFC shrinkage, pandas and AnnData (H5AD) integration, CSV export, volcano and MA plots, and a command-line script. Use when comparing gene expression between conditions such as treated vs control, adjusting for batch or covariates, porting an R DESeq2 workflow to Python, or building a Python pipeline for differential expression from raw integer counts. Not for single-cell data or for R-based DESeq2 itself.Example requests
Ask your agent in plain language, for example:- “How do I run DESeq2 in Python on my counts table?”
- “I have counts.csv (60k genes x 12 samples, STAR/featureCounts output, mouse liver) and meta.csv with columns condition (ctrl/KO) and batch (b1/b2/b3). I get ‘Index mismatch between counts and metadata’ when I build the dataset. Please fix it and give me KO vs ctrl results adjusted for batch, with a volcano plot.”
- “We sequenced 8 tumor and 8 normal biopsies and have a gene-level raw count matrix. I want a ranked list of genes that differ between tumor and normal, with fold changes that are shrunk so the noisy low-count genes don’t dominate the ranking, and a table I can send to a collaborator.”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole life-sciences category with /plugin install life-sciences@research-agent-skills.