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About pydeseq2
pydeseq2
Performs bulk RNA-seq differential expression analysis
pydeseq2 is an agent skill for bulk RNA-seq differential expression analysis with PyDESeq2. It fits negative-binomial generalised linear models, with explicit biological replicates, covariates and comparisons, and it covers count validation, formula designs, explicit contrasts, Wald tests and false discovery rate control. The skill is strict about the design. 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, but the skill is not a single-cell per-cell testing workflow. The current PyDESeq2 0.5.4 provides Wald tests and apeGLM-style coefficient shrinkage. The skill warns not to assume that every feature or numerical result of the R version of DESeq2 is implemented in the same way. Its reference files give the supported calls, the AnnData storage conventions and the limits of what was verified. Use it when you have a count matrix and sample metadata and need a defensible comparison between conditions. It is part of the k-dense-ai scientific agent skills repository on GitHub, which has about 49,600 stars.
- Price
- Free
- Made by
- k-dense-ai
- Category
- AI Skills
- Website
- github.com/k-dense-ai/scientific-agent-skills/pydeseq2