pydeseq2
by K-Dense-AIpydeseq2 is a Python DESeq2 skill for bulk RNA-seq differential gene expression analysis. Use it to compare conditions, fit single- or multi-factor designs, apply Wald tests and FDR correction, and generate volcano or MA plots in pandas and AnnData workflows.
This skill scores 80/100 and is worth listing. The repository gives directory users enough evidence that an agent can recognize when to use it, follow a real PyDESeq2 differential-expression workflow, and apply it with less guesswork than a generic prompt. It is not perfectly packaged, but it has substantial operational content and clear install-value for bulk RNA-seq analysis users.
- Strong triggerability: the frontmatter and "When to Use This Skill" explicitly target DESeq2, differential expression, bulk RNA-seq counts, and PyDESeq2 mentions.
- Operational workflow content: the skill body includes a concrete quick-start with pandas, DeseqDataSet, DeseqStats, filtering, Wald tests, and FDR correction.
- Good agent leverage: it covers single-factor and multi-factor designs, batch/covariate handling, apeGLM shrinkage, and pandas/AnnData integration.
- No install command or support files are provided, so users may need to infer environment/setup details themselves.
- The repository is marked with an experimental/test signal and appears to be a single SKILL.md without references or auxiliary assets.
Overview of pydeseq2 skill
What pydeseq2 is for
pydeseq2 is a Python skill for differential gene expression analysis on bulk RNA-seq count data. It helps you go from raw counts and sample metadata to DE results, fold changes, adjusted p-values, and standard exploratory outputs like volcano and MA plots.
Who should use it
Use the pydeseq2 skill if you want DESeq2-style analysis in Python, need multi-factor designs, or want to fit differential expression into a pandas/AnnData-based workflow. It is a good fit for analysts who already have count matrices and clinical or experimental metadata, not for users looking for a full RNA-seq preprocessing pipeline.
What makes it useful
The main value of pydeseq2 is that it reduces translation friction for Python users who would otherwise jump to R for DESeq2. It supports Wald testing, multiple-testing correction, optional apeGLM shrinkage, and a workflow that is practical for reproducible notebook or pipeline use.
How to Use pydeseq2 skill
Install pydeseq2
Install the skill in your Claude skill set, then open the skill files before prompting:
npx skills add K-Dense-AI/claude-scientific-skills --skill pydeseq2
For pydeseq2 install and setup decisions, verify that your environment already has the RNA-seq count table, sample metadata, and the Python packages required by your workflow. The skill is most useful when you can provide sample-by-gene counts and a design formula or grouping variable.
Start from the right inputs
Strong pydeseq2 usage starts with clean input structure:
- a count matrix with samples as rows and genes as columns
- metadata indexed by sample ID
- a clear condition column, and any batch or covariate columns you want in the model
- an explicit comparison target, such as treated vs control
A weak prompt says: “Run differential expression on my RNA-seq data.”
A stronger prompt says: “Use pydeseq2 on a bulk RNA-seq count matrix with 24 samples, compare treated vs control, include batch as a covariate, filter very low-count genes, and return significant genes plus volcano/MA plot code.”
Read these files first
Start with SKILL.md for the workflow and expected analysis steps. Then inspect README.md, AGENTS.md, metadata.json, and any rules/, resources/, references/, or scripts/ folders if present. For this repository, the main practical signal is in SKILL.md, so do not assume extra helper files exist.
Use pydeseq2 well
Treat pydeseq2 as an analysis method, not just a code generator. Tell the model:
- what organism and assay you have
- how samples are grouped
- whether you need single-factor or multi-factor design
- whether you want shrinkage, ranking, or visualization
- what output format you need, such as a dataframe, notebook cells, or a reusable script
This improves pydeseq2 usage because the model can choose the right design, filtering, and interpretation steps instead of guessing.
pydeseq2 skill FAQ
Is pydeseq2 only for DESeq2 users?
No. It is for anyone who wants DESeq2-like differential expression analysis in Python. It is especially useful if you already work in pandas, scanpy, or AnnData and want to keep the analysis in one stack.
Do I need a perfect prompt to use it?
No, but vague prompts lead to generic analysis code. The pydeseq2 skill works best when you provide the count table shape, comparison of interest, and any known confounders.
Is pydeseq2 beginner-friendly?
It is beginner-friendly if you already understand the basics of RNA-seq counts and experimental design. It is less suitable if you need help with alignment, quantification, or upstream QC before differential expression.
When should I not use pydeseq2?
Do not use it for single-cell differential expression, normalized expression without raw counts, or workflows that need a full end-to-end transcriptomics pipeline. It is also not the right choice if your real need is statistical interpretation without gene-level count data.
How to Improve pydeseq2 skill
Give better biological context
The best pydeseq2 results come from prompts that explain the study design, not just the file names. Include the response variable, control condition, batch effects, replicate count, and whether you want gene ranking, plot code, or interpretation.
Specify the analysis decisions you care about
Tell the skill how to handle low-count genes, whether to use a multi-factor model, and whether you need shrinkage for effect sizes. These choices materially affect pydeseq2 outputs and help avoid generic defaults that may not match your study.
Ask for output you can reuse
Instead of asking only for “results,” request a saved dataframe schema, a plotting snippet, or a notebook-ready workflow. For example: “Return pydeseq2 code that fits the model, extracts adjusted p-values, and writes a CSV of significant genes with log2 fold change and padj.”
Iterate from diagnostics, not just final hits
If the first run looks off, ask for QC-oriented checks: sample clustering, count filtering rationale, the number of genes retained, or whether the design formula is confounded. This is the fastest way to improve pydeseq2 for Data Analysis when results are weak or unexpectedly sparse.