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Using pmultiqc with MaxQuant Output

The maxquant plugin parses the standard text output files produced by MaxQuant and generates a QC report comparable to the quantms module. MaxQuant output directories typically contain a txt/ subfolder with all relevant files.

Supported Input Files

File Description
parameters.txt Analysis parameters and software settings
proteinGroups.txt Protein group identification and quantification
summary.txt Per-raw-file MS/MS identification summary
evidence.txt Peptide-level evidence with retention times and intensities
msms.txt MS/MS scan results with fragment ion information
msmsScans.txt Detailed MS/MS scan information
*sdrf.tsv SDRF-Proteomics metadata file (optional)

Place all files in the same directory (or in a txt/ subfolder). pmultiqc will locate them automatically.

Running the Report

# Basic usage
multiqc --maxquant-plugin /path/to/maxquant/results -o ./report

# Include SDRF for experimental design visualization
multiqc --maxquant-plugin /path/to/maxquant/results -o ./report

# Disable large protein/peptide tables for datasets with many samples
multiqc --maxquant-plugin /path/to/maxquant/results -o ./report --disable-table

# Customize the contaminant prefix (MaxQuant default is CON__)
multiqc --maxquant-plugin /path/to/maxquant/results -o ./report --contaminant-affix CON__

# Disable hover tooltips for static reporting
multiqc --maxquant-plugin /path/to/maxquant/results -o ./report --disable-hoverinfo

QC Sections Generated

Experimental Design

If an SDRF file is present alongside the MaxQuant output, pmultiqc renders an experimental design table showing sample-to-file mapping, biological replicates, fractions, and technical replicates.

Parameters Summary

Key parameters extracted from parameters.txt:

  • MaxQuant version
  • Enzyme and missed cleavages setting
  • Fixed and variable modifications
  • Mass tolerance (MS1 and MS2)
  • FDR thresholds

Protein Groups Statistics

From proteinGroups.txt:

  • Protein Identification Summary — total protein groups, unique proteins, and contaminant fraction
  • Potential Contaminants per Group — bar chart showing the proportion of contaminant signal per sample group
  • Number of Peptides per Protein — distribution of unique peptide counts per protein group

MS/MS Identification Statistics

From summary.txt:

  • MS/MS Identification Rate — per-raw-file identification percentage bar chart
  • Spectra Tracking — MS1 scans, MS2 scans, and identified PSM counts per file

Evidence-Level Metrics

From evidence.txt:

  • Charge State Distribution — frequency of precursor charge states across all identified peptides
  • Retention Time Distribution — histogram of peptide identifications over the LC gradient
  • Delta Mass — mass error distribution in Da and ppm
  • Peptide Intensity Distribution — log-transformed intensity histogram for LFQ or iBAQ values
  • Missed Cleavages — proportion of peptides with 0, 1, or 2 missed tryptic cleavages
  • Peptide Length Distribution — sequence length histogram

MS/MS Scan Metrics

From msms.txt and msmsScans.txt:

  • Search Engine Scores — Andromeda score distribution for identified PSMs
  • Peaks per MS2 Spectrum — histogram of fragment ion peak counts per spectrum
  • Peak Intensity Distribution — distribution of MS2 fragment ion intensities
  • Oversampling — analysis of precursors selected multiple times per LC run

Heatmap

Sample-by-sample correlation matrix computed from LFQ intensities in proteinGroups.txt. Useful for identifying outliers or unexpected batch structure.

File Detection

The MaxQuant module (pmultiqc/modules/maxquant/maxquant_io.py) searches for files using the following precedence:

  1. Direct file names in the analysis directory
  2. Files in a txt/ subdirectory
  3. SDRF file ending in sdrf.tsv in the same location

If none of the required files are found, the module is skipped and no MaxQuant section appears in the report.

Notes

  • MaxQuant LFQ intensities are used for quantification comparisons; iBAQ values are also parsed when present.
  • The msmsScans.txt file is optional but enables additional scan-level metrics when present.
  • For TMT or SILAC experiments run in MaxQuant, the relevant intensity columns are detected automatically from the column headers.