pmultiqc 0.0.33

Python package for quality control of proteomics datasets, based on multiqc package

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

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.

Development

To contribute to pmultiqc:

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