---

name: statistics-and-results description: "Compute, interpret, export, and troubleshoot PyDESeq2 statistical results after fitting a DeseqDataSet." disable-model-invocation: true metadata: disco-role: operating license: MIT

PyDESeq2 Statistics and Results

Use this sub-skill after a DeseqDataSet has already been fitted with dds.deseq2() or the equivalent staged fitting steps. It covers DeseqStats, contrasts, Wald-test summaries, adjusted p-values, log-fold-change shrinkage, MA plots, and result-table interpretation.

When To Use

• Build a DeseqStats(dds, contrast=...) object for a fitted DeseqDataSet.
• Compare categorical levels with contrast=[factor, tested_level, reference_level] or use a numeric contrast vector for direct design matrices.
• Run summary(), thresholded tests with lfc_null and alt_hypothesis, or run_wald_test() when a lower-level rerun is needed.
• Interpret results_df columns, p-value filtering, NaN behavior, coefficient names, and log2 versus natural-log scales.
• Apply lfc_shrink(coeff, adapt=True) and make plot_MA(log=True, save_path=None, **kwargs) outputs.

Route Elsewhere

• For raw count/metadata loading, CSV/index validation, AnnData setup, and design-matrix input checks, use ../data-io-validation/SKILL.md.
• For complete fitting workflows that create the fitted DeseqDataSet, use ../dea-workflows/SKILL.md.
• For staged normalization, dispersion, LFC fitting, VST, and internal AnnData keys, use ../model-fitting-internals/SKILL.md.

Minimal Result Pattern

from pydeseq2.ds import DeseqStats

stat_res = DeseqStats(
    dds,
    contrast=["condition", "B", "A"],
    alpha=0.05,
    cooks_filter=True,
    independent_filter=True,
    quiet=True,
)
stat_res.summary()
results = stat_res.results_df.sort_values("padj", na_position="last")

Use results_df for exports and ranking. It contains baseMean, log2FoldChange, lfcSE, stat, pvalue, and padj after summary().

Key References

• references/results-reference.md gives result-column semantics, contrast forms, thresholded tests, shrinkage behavior, coefficient naming, and MA-plot usage.
• references/troubleshooting.md maps common runtime errors and confusing outputs to fixes.
• scripts/summarize_results.py runs a safe synthetic workflow with flags for contrast, thresholded tests, shrinkage, MA plotting, CSV export, and independent filtering.

Safe Script

Run the bundled script after installing PyDESeq2. From the pydeseq2 skill root, use:

python scripts/summarize_results.py --help
python scripts/summarize_results.py --contrast condition B A --output-csv results.csv --ma-plot ma.png

The script uses PyDESeq2's packaged synthetic dataset by default, performs a small fit, and does not download data or mutate files unless explicit output paths are provided.