Version Compatibility

Reference examples tested with: pandas 2.2+, numpy 1.26+, scipy 1.12+, matplotlib 3.8+, scikit-learn 1.4+, limma 3.58+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Proteomics Quality Control -- A Three-Level Funnel Where the Matrix Sees the Failure Last

"Check the quality of my proteomics data" -> Read instrument, identification, and quantitative metrics as a descending funnel of silent failures, and inspect raw signal BEFORE normalizing -- because by the time a fault reaches the deliverable matrix, normalization has usually erased the evidence.

• Python: pandas for matrix QC; matplotlib/seaborn for raw boxplots, correlation heatmaps, PCA
• R: PTXQC createReport() for MaxQuant search-table QC; limma::plotMDS()/plotDensities(); MSstatsTMT dataProcessPlotsTMT() for TMT channel balance

Scope: This skill OWNS QC diagnosis across all three levels -- which metric localizes which fault, what threshold means trouble, and the mandatory inspect-before-normalize ordering. Normalization mechanics route to quantification; the differential test routes to differential-abundance; DIA q-value computation routes to dia-analysis. OUT OF SCOPE: running the statistical test, the normalization algorithms themselves, and DIA q-value/FDR internals.