User Guide

This section covers the practical usage of each mokume command, with complete examples for both CLI and Python API.

Commands

features2proteins: Unified Pipeline

The recommended entry point. Takes raw feature data and produces a protein quantification matrix in one step, with optional normalization, batch correction, IRS, differential expression, and visualization.

features2peptides: Peptide Normalization

Normalizes and filters feature-level data into peptide intensities. Use this when you need fine-grained control over the normalization step before quantification.

peptides2protein: Protein Quantification

Quantifies proteins from normalized peptide data. Supports iBAQ (with TPA and ProteomicRuler), TopN, MaxLFQ, DirectLFQ, and Sum.

batch-correct: Batch Correction

Standalone batch correction for already-quantified protein data. Combines multiple files and applies ComBat correction.

Visualization & Reports

PCA, t-SNE, volcano plots, heatmaps, and interactive HTML QC reports.