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pydeseq2

8.7

by davila7

92Favorites
294Upvotes
0Downvotes

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

bioinformatics

8.7

Rating

0

Installs

Data & Analytics

Category

Quick Review

Excellent skill for differential gene expression analysis with PyDESeq2. The description clearly conveys capabilities enabling CLI agents to invoke it appropriately. Task knowledge is comprehensive with complete workflows, code examples, troubleshooting, and references to additional files (api_reference.md, workflow_guide.md, run_deseq2_analysis.py). Structure is well-organized with clear sections progressing from quick start to advanced patterns, though SKILL.md is somewhat lengthy. Novelty is strong: DESeq2 analysis requires specialized bioinformatics knowledge, multiple interdependent steps (normalization, dispersion estimation, Wald tests, FDR correction), and significant tokens for a CLI agent to implement correctly. This skill meaningfully reduces cognitive load and execution cost for RNA-seq differential expression workflows. Minor improvement possible by offloading more detail to reference files, but current organization remains clear and functional.

LLM Signals

Description coverage9
Task knowledge10
Structure9
Novelty8

GitHub Signals

18,073
1,635
132
71
Last commit 0 days ago

Publisher

davila7

davila7

Skill Author

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davila7

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