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Quick Start

This guide shows how to go from raw feature data to protein intensities using mokume.

Prerequisites

You need:

  1. A parquet file in quantms.io/qpx format (output from quantms pipeline)
  2. Optionally, an SDRF file for sample metadata

The features2proteins command handles everything: loading, filtering, normalization, and quantification.

# MaxLFQ quantification (default)
mokume features2proteins \
    -p features.parquet \
    -o proteins.csv \
    -s experiment.sdrf.tsv

# With TMT IRS normalization + differential expression
mokume features2proteins \
    -p features.parquet \
    -o proteins.csv \
    -s experiment.sdrf.tsv \
    --quant-method median \
    --irs --irs-remove-reference \
    --de --de-contrasts "NASH-HL" \
    --plot-dir plots/ --plot-volcano --plot-pca

# DirectLFQ (uses directlfq package for everything)
mokume features2proteins \
    -p features.parquet \
    -o proteins.csv \
    --quant-method directlfq

# iBAQ (requires FASTA)
mokume features2proteins \
    -p features.parquet \
    -o proteins.csv \
    --quant-method ibaq \
    --fasta proteome.fasta

from mokume.pipeline import features_to_proteins

# Simple MaxLFQ
proteins = features_to_proteins(
    parquet="features.parquet",
    output="proteins.csv",
    sdrf="experiment.sdrf.tsv",
    quant_method="maxlfq",
)

# TMT with IRS + DE
proteins = features_to_proteins(
    parquet="features.parquet",
    output="proteins.csv",
    sdrf="experiment.sdrf.tsv",
    quant_method="median",
    irs=True,
    irs_remove_reference=True,
    differential_expression=True,
    de_contrasts=["NASH-HL"],
)

Individual Quantification Methods

If you already have normalized peptide data, use the quantification classes directly:

import pandas as pd
from mokume.quantification import (
    TopNQuantification,
    MaxLFQQuantification,
    AllPeptidesQuantification,
    get_quantification_method,
)

peptides = pd.read_csv("peptides.csv")

# Top3 quantification
top3 = TopNQuantification(n=3)
result = top3.quantify(
    peptides,
    protein_column="ProteinName",
    peptide_column="PeptideSequence",
    intensity_column="NormIntensity",
    sample_column="SampleID",
)

# MaxLFQ (auto-uses DirectLFQ if installed)
maxlfq = MaxLFQQuantification(min_peptides=2, threads=4)
result = maxlfq.quantify(peptides)

# Factory function (parses method name)
method = get_quantification_method("top5")  # TopNQuantification(n=5)
result = method.quantify(peptides)

Two-Step Pipeline

For more control, use the peptide normalization step separately:

# Step 1: Normalize peptides
mokume features2peptides \
    -p features.parquet \
    -s experiment.sdrf.tsv \
    --nmethod median \
    --pnmethod globalMedian \
    --output peptides.csv

# Step 2: Quantify proteins
mokume peptides2protein \
    --method maxlfq \
    -p peptides.csv \
    -o proteins.tsv

What's Next?