Performs end-to-end differential expression analysis for LFQ proteomics: input parsing for MaxQuant and DIA-NN outputs, filtering of contaminants, log2 transformation, down-shifted Gaussian imputation, statistical testing (two-sample t-test with s0-based FDR correction), and diagnostic visualizations (PCA, volcano, imputation diagnostics). Produces a reproducible report directory with figures and tables.
Use this skill when you have processed proteinGroups (MaxQuant) or DIA-NN output and need a reproducible DE analysis pipeline (e.g., quick exploratory DE on treatment vs control contrasts). Not for raw MS processing or biological interpretation — it's an analysis/visualization pipeline that requires user review of results.
proteomics_de.py demo script present in the repoAgents able to run Python data workflows (CLI-capable agents, local OpenClaw runtimes, or Code-focused agents with python3 available).
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