Differential gene expression analysis for bulk RNA-seq with PyDESeq2, including formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization.
Installs just this skill. Get the whole plugin for auto-invocation.
⚡ How it fires
How this skill gets triggered: by you, by Claude, or both.
Fires itselfClaude auto-loads it when your prompt matches the work.
You can call itInvoke it directly when you want it.
Slash command/pydeseq2
👁️ Context preview
The summary Claude sees to decide when to auto-load this skill.
Differential gene expression analysis for bulk RNA-seq with PyDESeq2, including formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization.
📊 Stats
Stars31,549
Forks3,146
LanguagePython
LicenseMIT
📦 Ships with scientific-agent-skills
</> SKILL.md
pydeseq2.SKILL.md
---name: pydeseq2
description: Differential gene expression analysis for bulk RNA-seq with PyDESeq2, including formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization.
allowed-tools: Read Write Edit Bash
compatibility: Requires Python >=3.11 and PyDESeq2 0.5.4-compatible dependencies. Examples target PyDESeq2 0.5.x, formulaic design strings, explicit contrasts, and uv-based installs.
license: MIT license
metadata: {"version": "1.1", "skill-author": "K-Dense Inc."}
---# PyDESeq2
## Overview
PyDESeq2 is a Python implementation of DESeq2 for differential expression analysis with bulk RNA-seq data. Design and execute complete workflows from data loading through result interpretation, including formulaic single-factor and multi-factor designs, Wald tests with multiple testing correction, optional apeGLM shrinkage, and integration with pandas and AnnData.
## When to Use This Skill
This skill should be used when:
- Analyzing bulk RNA-seq count data for differential expression
- Comparing gene expression between experimental conditions (e.g., treated vs control)
- Performing multi-factor designs accounting for batch effects or covariates
- Converting R-based DESeq2 workflows to Python
- Integrating differential expression analysis into Python-based pipelines
- Users mention "DESeq2", "differential expression", "RNA-seq analysis", or "PyDESeq2"
- Use formulaic/Wilkinson formula notation (R-style)
- Put adjustment variables (e.g., batch) before the main variable of interest
- Ensure variables exist as columns in the metadata DataFrame
- Use appropriate data types; continuous variables are detected from the formula, and categorical variables can be forced with `C(variable)` or a pandas categorical dtype
- Do not use deprecated `design_factors`, `continuous_factors`, or `ref_level` arguments in new workflows
### Step 3: DESeq2 Fitting
Initialize the DeseqDataSet and run the complete pipeline:
```python
from pydeseq2.dds import DeseqDataSet
from pydeseq2.default_inference import DefaultInference
- When prioritizing genes for follow-up experiments
**Important:** Shrinkage affects only the log2FoldChange values, not the statistical test results (p-values remain unchanged). Use shrunk values for visualization but report unshrunken p-values for significance.
Avoid loading pickle files from untrusted sources. For exchange between agents, pipelines, or collaborators, prefer CSV results and `.h5ad` AnnData files.
design = "~condition + batch + condition:batch" # Model interaction
```
### No Significant Genes
**Diagnostics:**
```python
# Check dispersion distribution
plt.hist(dds.var["dispersions"], bins=50)
plt.show()
# Check size factors
print(dds.obs["size_factors"])
# Look at top genes by raw p-value
print(ds.results_df.nsmallest(20, "pvalue"))
```
**Possible causes:**
- Small effect sizes
- High biological variability
- Insufficient sample size
- Technical issues (batch effects, outliers)
## Reference Documentation
For comprehensive details beyond this workflow-oriented guide:
- **API Reference** (`references/api_reference.md`): Complete documentation of PyDESeq2 classes, methods, and data structures. Use when needing detailed parameter information or understanding object attributes.
- **Workflow Guide** (`references/workflow_guide.md`): In-depth guide covering complete analysis workflows, data loading patterns, multi-factor designs, troubleshooting, and best practices. Use when handling complex experimental designs or encountering issues.
Load these references into context when users need:
- Detailed API documentation: `Read references/api_reference.md`
- Troubleshooting guidance: `Read references/workflow_guide.md` (see Troubleshooting section)
## Key Reminders
1. **Data orientation matters:** Count matrices typically load as genes × samples but need to be samples × genes. Always transpose with `.T` if needed.
2. **Sample filtering:** Remove samples with missing metadata before analysis to avoid errors.
3. **Gene filtering:** Filter low-count genes (e.g., < 10 total reads) to improve power and reduce computational time.
4. **Design formula order:** Put adjustment variables before the variable of interest (e.g., `"~batch + condition"` not `"~condition + batch"`).
5. **LFC shrinkage timing:** Apply shrinkage after statistical testing and only for visualization/ranking purposes. P-values remain based on unshrunken estimates.
6. **Result interpretation:** Use `padj < 0.05` for significance, not raw p-values. The Benjamini-Hochberg procedure controls false discovery rate.
7. **Contrast specification:** The format is `[variable, test_level, reference_level]` where test_level is compared against reference_level.
8. **Save intermediate objects:** Prefer `dds.to_picklable_anndata().write_h5ad("dds_result.h5ad")` for portable outputs. Only load pickle files that you created yourself and trust.
