Perform differential expression analysis on bulk RNA-seq count data.
npx skills add https://github.com/ovachiever/droid-tings --skill pydeseq2
Please help me install this Agent Skill. Skill: pydeseq2 Source: https://github.com/ovachiever/droid-tings/tree/main/skills/pydeseq2 Command: npx skills add https://github.com/ovachiever/droid-tings --skill pydeseq2
PyDESeq2 provides a Python implementation of DESeq2 for differential expression analysis on bulk RNA-seq data, enabling Wald tests, multiple testing correction, and LFC shrinkage in Python.
Load counts and metadata, run DESeq2 pipeline, and extract significant genes.
High-intent search queries and answers about installing and using this skill.
Differential expression analysis identifies genes with significant expression changes between conditions. PyDESeq2 applies DESeq2-style normalization, dispersion estimation, Wald tests with BH-FDR correction, and log2 fold-change calculation to bulk RNA-seq counts, producing a results dataframe with p-values and adjusted significance for each gene.
Yes, PyDESeq2 handles both single-factor and multi-factor designs. It applies Wald tests and BH-FDR correction across complex experimental layouts, integrating with pandas and AnnData to manage metadata and design matrices for end-to-end workflows.
PyDESeq2 uses Cook's distance to detect outlier samples and observations. This helps flag potentially artifactual counts before downstream interpretation, improving the reliability of differential expression results.
Yes, PyDESeq2 integrates directly with pandas DataFrames for count matrices and metadata, and with AnnData for annotated data objects. This enables seamless end-to-end workflows from raw counts through results export.
Log fold-change shrinkage via apeGLM reduces noise in LFC estimates, particularly for lowly-expressed genes. It improves visualization and interpretation of results by producing more stable effect-size estimates for downstream analysis.
PyDESeq2 generates a results dataframe (results_df) containing p-values, adjusted p-values, log fold-changes, and per-gene statistics. Export this for filtering by FDR threshold, fold-change cutoff, or downstream functional annotation.
LeonChaoXRun bulk RNA-seq differential expression with Wald statistics and FDR-adjusted p-values.
ya-wayAnalyzes Reactome pathway enrichment for gene lists using enrichPathway and gsePathway.
yf8578Manage annotated data matrices for single-cell genomics using the AnnData Python package.
MubasherMohammedSummarize differential expression results from DESeq2, edgeR, or limma outputs.
