Individual skill
pydeseq2
Performs bulk RNA-seq differential expression analysis with PyDESeq2, including count validation, formula designs, explicit contrasts, Wald tests, FDR correction, coefficient-matched LFC shrinkage, and result visualization. Use for PyDESeq2 or Python DESeq2 workflows with biological replicates.
skills/pydeseq2/SKILL.md
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- Last checked
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- License
- MIT
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SKILL.mdcore · 10,507 bytesSKILL.mdcore · 10,507 bytesSKILL.mdcore · 10,507 bytesSKILL.mdcore · 10,507 bytesSKILL.mdcore · 10,507 bytesSKILL.mdcore · 10,507 bytesreferences/analysis_patterns.mddoc · 5,188 bytesreferences/api_reference.mddoc · 8,990 bytesreferences/core_workflow_steps.mddoc · 3,493 bytesreferences/workflow_guide.mddoc · 9,179 bytesscripts/run_deseq2_analysis.pyruns · 17,909 bytes
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# PyDESeq2
Use this skill for bulk RNA-seq negative-binomial GLMs, with explicit biological
replicates, covariates, and comparisons. Cells, sequencing lanes, and repeated
measurements from the same donor are not independent biological replicates.
Donor-level pseudobulk can be appropriate when its aggregation and design match
the scientific question; this is not a single-cell per-cell testing workflow.
Current [PyDESeq2 0.5.4](https://github.com/scverse/PyDESeq2/releases/tag/v0.5.4)
provides Wald tests and apeGLM-style coefficient shrinkage. Do not assume every
feature or numerical result of current R DESeq2 is implemented identically.
See [API and tested versions](references/api_reference.md) for supported calls,
AnnData storage, and source/runtime verification limits.
