Identify differentially expressed genes from bulk RNA-seq counts with PyDESeq2.
npx skills add https://github.com/OwnLabAI/ownlab --skill pydeseq2-ownlabai
Please help me install this Agent Skill. Skill: pydeseq2 Source: https://github.com/OwnLabAI/ownlab/tree/main/mart/skills/scientific-skills/pydeseq2 Command: npx skills add https://github.com/OwnLabAI/ownlab --skill pydeseq2-ownlabai
PyDESeq2 provides a Python-based, end-to-end workflow for differential expression analysis of bulk RNA-seq data, enabling researchers to perform DE testing in a single, reproducible environment.
Run the included run_deseq2_analysis.py script with your counts and metadata to perform a full differential expression analysis.
High-intent search queries and answers about installing and using this skill.
To perform differential expression analysis on bulk RNA-seq counts in Python, you can use this workflow to normalize data, estimate dispersion, fit log fold-changes, and run Wald tests. It processes counts and sample metadata to identify differentially expressed genes.
Yes, you can handle batch effects and covariates in RNA-seq differential expression analysis using multi-factor designs. The workflow supports applying sample metadata to isolate specific treatment effects while controlling for confounding variables across cohorts.
The best way to run DESeq2 analysis without switching to R is using a Python-based pipeline that replicates the process. It applies normalization, dispersion estimation, multiple testing correction, and optional shrinkage to export results directly in Python.
No, you do not need pre-normalized RNA-seq counts to start differential expression testing. The workflow performs its own normalization on raw bulk RNA-seq count matrices alongside sample metadata to ensure accurate dispersion estimation and log fold-change fitting.
Dispersion estimation matters for identifying differentially expressed genes because it models the variance in RNA-seq counts across biological replicates. Accurate dispersion fitting ensures reliable Wald tests and valid multiple testing corrections for differential expression results.
A limitation of using Python for RNA-seq differential expression analysis is the dependency on the pydeseq2 library, which requires structured counts and sample metadata DataFrames. It focuses on bulk RNA-seq and does not process single-cell transcriptomics data.
Victory-HugoPerform differential expression analysis on bulk RNA-seq data with PyDESeq2.
tassiovaleAnalyze gene lists or ranked data for enriched biological pathways and gene sets.
bigbioBuild standardized AnnData objects from EBI Expression Atlas bulk-RNA-seq TPM matrices and SDRF metadata.
ovachieverPerform differential expression analysis on bulk RNA-seq count data.
