pmultiqc

What is pmultiqc?

pmultiqc is a MultiQC plugin for comprehensive quality control reporting of proteomics data. It generates interactive HTML reports with visualizations and metrics to help you assess the quality of your mass spectrometry-based proteomics experiments.

Key Features

• Works with multiple proteomics data formats and analysis pipelines
• Generates interactive HTML reports with visualizations
• Provides comprehensive QC metrics for MS data
• Supports different quantification methods (LFQ, TMT, DIA)
• Integrates with the MultiQC framework

Supported Data Sources

pmultiqc supports the following data sources:

1. quantms pipeline output files:

   • experimental_design.tsv: Experimental design file
   • *.mzTab: Results of the identification
   • *msstats*.csv: MSstats/MSstatsTMT input files
   • *.mzML: Spectra files
   • *ms_info.tsv: MS quality control information
   • *.idXML: Identification results
   • *.yml: Pipeline parameters (optional)
   • diann_report.tsv or diann_report.parquet: DIA-NN main report (DIA analysis only)

2. MaxQuant result files:

   • parameters.txt: Analysis parameters
   • proteinGroups.txt: Protein identification results
   • summary.txt: Summary statistics
   • evidence.txt: Peptide evidence
   • msms.txt: MS/MS scan information
   • msmsScans.txt: MS/MS scan details
   • *sdrf.tsv: SDRF-Proteomics (optional)

3. DIA-NN result files:

   • report.tsv or report.parquet: DIA-NN main report
   • experimental_design.tsv or *sdrf.tsv: Experimental design file / SDRF-Proteomics (optional)
   • *ms_info.parquet: mzML statistics after Raw-to-mzML conversion (using quantms-utils) (optional)

4. ProteoBench file:

   • result_performance.csv: ProteoBench result file

5. mzIdentML files:

   • *.mzid: Identification results
   • *.mzML or *.mgf: Corresponding spectra files

Installation

Install from PyPI

# To install the stable release from PyPI:
pip install pmultiqc

Install from Source (Without PyPI)

# Fork the repository on GitHub

# Clone the repository
git clone https://github.com/your-username/pmultiqc.git
cd pmultiqc

# Install the package locally
pip install .

# Now you can run pmultiqc on your own dataset

Usage

pmultiqc is used as a plugin for MultiQC. After installation, you can run it using the MultiQC command-line interface.

Basic Usage

multiqc {analysis_dir} -o {output_dir}

Where:

• {analysis_dir} is the directory containing your proteomics data files
• {output_dir} is the directory where you want to save the report

Examples

For quantms pipeline results

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

# With specific options
multiqc /path/to/quantms/results -o ./report --remove_decoy --condition factor

For MaxQuant results

multiqc --parse_maxquant /path/to/maxquant/results -o ./report

For DIA-NN results

multiqc /path/to/diann/results -o ./report

For ProteoBench files

multiqc --parse_proteobench /path/to/proteobench/files -o ./report

For mzIdentML files

multiqc --mzid_plugin /path/to/mzid/files -o ./report

Command-line Options

Option	Description	Default
--raw	Keep filenames in experimental design output as raw	False
--condition	Create conditions from provided columns	-
--remove_decoy	Remove decoy peptides when counting	True
--decoy_affix	Pre- or suffix of decoy proteins in their accession	DECOY_
--contaminant_affix	The contaminant prefix or suffix	CONT
--affix_type	Location of the decoy marker (prefix or suffix)	prefix
--disable_plugin	Disable pmultiqc plugin	False
--quantification_method	Quantification method for LFQ experiment	feature_intensity
--disable_table	Disable protein/peptide table plots for large datasets	False
--ignored_idxml	Ignore idXML files for faster processing	False
--parse_maxquant	Generate reports based on MaxQuant results	False
--parse_proteobench	Generate reports based on ProteoBench result	False
--mzid_plugin	Generate reports based on mzIdentML files	False

QC Metrics and Visualizations

pmultiqc generates a comprehensive report with multiple sections:

General Report

• Experimental Design: Overview of the dataset structure
• Pipeline Performance Overview: Key metrics including:
  • Contaminants Score
  • Peptide Intensity
  • Charge Score
  • Missed Cleavages
  • ID rate over RT
  • MS2 OverSampling
  • Peptide Missing Value
• Summary Table: Spectra counts, identification rates, peptide and protein counts
• MS1 Information: Quality metrics at MS1 level
• Pipeline Results Statistics: Overall identification results
• Number of Peptides per Protein: Distribution of peptide counts per protein

Results Tables

• Peptide Table: First 500 peptides in the dataset
• PSM Table: First 500 PSMs (Peptide-Spectrum Matches)

Identification Statistics

• Spectra Tracking: Summary of identification results by file
• Search Engine Scores: Distribution of search engine scores
• Precursor Charges Distribution: Distribution of precursor ion charges
• Number of Peaks per MS/MS Spectrum: Peak count distribution
• Peak Intensity Distribution: MS2 peak intensity distribution
• Oversampling Distribution: Analysis of MS2 oversampling
• Delta Mass: Mass accuracy distribution
• Peptide/Protein Quantification Tables: Quantitative levels across conditions

Example Reports

You can find example reports on the docs page.

Reporting Issues

We have comprehensive issue templates to help you report problems effectively:

• Bug Reports: For crashes, incorrect metrics, or unexpected behavior
• Metric Requests: For new proteomics quality control metrics (we actively encourage these!)
• Feature Requests: For new visualizations, data format support, or functionality
• Service Issues: For problems with the PRIDE web service
• General Issues: For questions, suggestions, or issues that don't fit other categories

Contributing

We welcome contributions! See our Contributing Guide for detailed instructions.

Quick Start for Contributors

1. Fork the repository
2. Clone your fork: git clone https://github.com/YOUR-USERNAME/pmultiqc
3. Create a feature branch: git checkout -b new-feature
4. Make your changes
5. Install in development mode: pip install -e .
6. Test your changes: cd tests && multiqc resources/LFQ -o ./
7. Commit your changes: git commit -am 'Add new feature'
8. Push to the branch: git push origin new-feature
9. Submit a pull request

License

This project is licensed under the terms of the LICENSE file included in the repository.

Citation

If you use pmultiqc in your research, please cite:

pmultiqc: A MultiQC plugin for proteomics quality control
https://github.com/bigbio/pmultiqc