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Using pmultiqc with quantms Pipeline Output

The quantms plugin is the primary integration point between pmultiqc and the quantms pipeline. It parses mzTab, mzML, idXML, and experimental design files produced by quantms DDA workflows (LFQ and TMT/iTRAQ).

DIA experiments use a different plugin

For DIA experiments processed with quantmsdiann, use the --diann-plugin instead. See the DIA-NN Plugin Guide. DIA output is report.tsv/report.parquet — not mzTab.

Supported Input Files

The quantms module recognizes the following files from a quantms DDA results directory:

File Pattern Description
experimental_design.tsv Experimental design linking raw files to samples
*.mzTab DDA identification and quantification results
*msstats*.csv MSstats or MSstatsTMT input tables
*.mzML Spectrum files for MS1/MS2 signal inspection
*ms_info.tsv Precomputed MS quality control information
*.idXML OpenMS identification results (optional)
*.yml Pipeline parameter file (optional)

Running the Report

# Basic usage — point to the quantms results directory
multiqc --quantms-plugin /path/to/quantms/results -o ./report

# Remove decoy hits before counting (default: enabled)
multiqc --quantms-plugin results/ -o report/ --remove-decoy

# Use a custom decoy prefix
multiqc --quantms-plugin results/ -o report/ --decoy-affix REV_ --affix-type prefix

# Assign conditions from an SDRF factor column
multiqc --quantms-plugin results/ -o report/ --condition "factor value[disease]"

# Use spectral counting instead of feature intensity for LFQ quantification
multiqc --quantms-plugin results/ -o report/ --quantification-method spectral_counting

# Skip idXML parsing for faster execution on large datasets
multiqc --quantms-plugin results/ -o report/ --ignored-idxml

# Disable protein/peptide tables on very large datasets
multiqc --quantms-plugin results/ -o report/ --disable-table

QC Sections Generated

Experimental Design

An interactive table mapping each raw file to its sample, biological replicate, fraction, and technical replicate as defined in the SDRF/experimental design file.

Pipeline Performance Overview

A sparkline summary panel showing per-sample scores for:

  • Contaminant Score — fraction of total signal attributable to common contaminants (e.g., keratins, trypsin)
  • Peptide Intensity — distribution uniformity of peptide intensities across samples
  • Charge Score — expected vs. observed charge state composition
  • Missed Cleavages — proportion of peptides with 0, 1, or 2 missed cleavages
  • ID Rate over RT — identification rate across the retention time gradient
  • MS2 Oversampling — fraction of precursors selected for MS2 more than once in the same run
  • Peptide Missing Values — fraction of missing quantification values per sample

MS1 Information

Chromatographic and spectral quality metrics extracted from mzML files:

  • Total Ion Chromatograms (TIC) — ion signal over retention time per sample
  • Base Peak Chromatograms (BPC) — most abundant ion at each RT point
  • MS1 Peak Count — number of survey scans per file

Identification Statistics

  • Spectra Tracking — per-file counts of MS2 spectra, identified PSMs, and identification rate
  • MS/MS Identification Rate — bar chart of identification percentage by run and by sample
  • Search Engine Scores — distribution of PSM-level identification scores
  • Delta Mass (Da / ppm) — mass accuracy distribution for all identified PSMs
  • Precursor Charge Distribution — frequency of charge states +1 through +6+
  • Peaks per MS2 Spectrum — histogram of fragment ion counts per spectrum
  • Peak Intensity Distribution — histogram of MS2 peak intensities

Peptide and Protein Results

  • Identifications Summary Table — protein and peptide counts per sample
  • Number of Peptides per Protein — distribution of peptides assigned per protein group
  • Peptide Length Distribution — frequency of identified peptide sequence lengths
  • Peptide Intensity Table — quantification values across conditions (first 500 peptides)
  • PSM Table — per-PSM metrics for the first 500 entries
  • Long-Trend Plots — cumulative identification and quantification trends

Contaminants

  • Potential Contaminants — per-sample percentage of signal from contaminant proteins
  • Top N Contaminants — ranked list of the most abundant contaminant proteins detected

Heatmap

A sample-by-sample correlation heatmap based on peptide or protein quantification values, useful for detecting batch effects and outlier samples.

Notes

  • The quantms module requires at least one mzTab file to produce identification-level statistics.
  • DIA experiments are handled by the dedicated quantmsdiann pipeline and the --diann-plugin. See the DIA-NN Plugin Guide.
  • For TMT experiments, MSstatsTMT input files are parsed for normalization and quantification summaries.