Version Compatibility

Reference examples tested with: limma 3.58+, DEqMS 1.20+, proDA 1.20+, ashr 2.2+, pandas 2.2+, scipy 1.12+, statsmodels 0.14+

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.

Differential Protein Abundance -- Moderated Testing on a Log-Intensity Matrix with Honest Missingness

"Find differentially abundant proteins between my conditions" -> Moderated statistical testing on a normalized log-intensity matrix, carrying missingness in the likelihood instead of filling it in -- because the missing values are low BECAUSE the protein is low, and imputing them manufactures false positives.

• R: limma::eBayes(fit, trend=TRUE, robust=TRUE) for empirical-Bayes moderated t-tests (the protein-level workhorse)
• R: DEqMS::spectraCounteBayes() when PSM/peptide counts are available (preferred over limma-trend when quant depth varies)
• R: proDA::test_diff() / msqrob2 / MSstats when missing values are extensive (model the dropout, no imputation)
• Python: scipy.stats.ttest_ind(equal_var=False) + statsmodels BH (large n only; no moderation)