Proteomics#
Tutorials for the omicverse.protein module — downstream analysis of bulk quantitative
proteomics: label-free LC-MS/MS (MaxQuant, DIA-NN, FragPipe) and affinity proteomics
(Olink NPX). Covers QC, missing-value diagnosis and MCAR/MNAR classification, normalization,
peptide → protein summarization, imputation, differential expression (DEqMS, proDA, MSstats,
limma), and functional enrichment. Every tutorial runs on a real published dataset served
through ov.datasets. The statistical engines are the standalone R-parity packages
pyimputelcmd, pydeqms, pyproda, pymsstats and pyolinkanalyze.