# pmultiqc
[](https://github.com/bigbio/pmultiqc/actions/workflows/python-app.yml)
[](https://github.com/bigbio/pmultiqc/actions/workflows/python-publish.yml)
## 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](https://github.com/nf-core/quantms)** 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](https://www.maxquant.org)** 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](https://aptila.bio)** result files:
- `*ms_info.parquet`: mzML statistics after Raw-to-mzML conversion (using **[quantms-utils](https://github.com/bigbio/quantms-utils)**)
- `report.tsv` or `report.parquet`: DIA-NN main report
4. **[ProteoBench](https://proteobench.readthedocs.io)** file:
- `result_performance.csv`: ProteoBench result file
5. **mzIdentML** files:
- `*.mzid`: Identification results
- `*.mzML` or `*.mgf`: Corresponding spectra files
## Installation
### Install from PyPI
```bash
# To install the stable release from PyPI:
pip install pmultiqc
```
### Install from Source (Without PyPI)
```bash
# 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
```bash
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
```bash
# 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
```bash
multiqc --parse_maxquant /path/to/maxquant/results -o ./report
```
#### For DIA-NN results
```bash
multiqc /path/to/diann/results -o ./report
```
#### For ProteoBench files
```bash
multiqc --parse_proteobench /path/to/proteobench/files -o ./report
```
#### For mzIdentML files
```bash
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](https://bigbio.github.io/pmultiqc).
## 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
```
Raw data
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"description": "# pmultiqc\n\n[](https://github.com/bigbio/pmultiqc/actions/workflows/python-app.yml)\n[](https://github.com/bigbio/pmultiqc/actions/workflows/python-publish.yml)\n\n\n\n\n\n## What is pmultiqc?\n\npmultiqc is a MultiQC plugin for comprehensive quality control reporting of proteomics data. 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After installation, you can run it using the MultiQC command-line interface.\n\n### Basic Usage\n\n```bash\nmultiqc {analysis_dir} -o {output_dir}\n```\n\nWhere:\n- `{analysis_dir}` is the directory containing your proteomics data files\n- `{output_dir}` is the directory where you want to save the report\n\n### Examples\n\n#### For quantms pipeline results\n\n```bash\n# Basic usage\nmultiqc /path/to/quantms/results -o ./report\n\n# With specific options\nmultiqc /path/to/quantms/results -o ./report --remove_decoy --condition factor\n```\n\n#### For MaxQuant results\n\n```bash\nmultiqc --parse_maxquant /path/to/maxquant/results -o ./report\n```\n\n#### For DIA-NN results\n\n```bash\nmultiqc /path/to/diann/results -o ./report\n```\n\n#### For ProteoBench files\n\n```bash\nmultiqc --parse_proteobench /path/to/proteobench/files -o ./report\n```\n\n#### For mzIdentML files\n\n```bash\nmultiqc --mzid_plugin /path/to/mzid/files -o ./report\n```\n\n\n### Command-line Options\n\n| Option | Description | Default |\n|--------|-------------|---------|\n| `--raw` | Keep filenames in experimental design output as raw | `False` |\n| `--condition` | Create conditions from provided columns | - |\n| `--remove_decoy` | Remove decoy peptides when counting | `True` |\n| `--decoy_affix` | Pre- or suffix of decoy proteins in their accession | `DECOY_` |\n| `--contaminant_affix` | The contaminant prefix or suffix | `CONT` |\n| `--affix_type` | Location of the decoy marker (prefix or suffix) | `prefix` |\n| `--disable_plugin` | Disable pmultiqc plugin | `False` |\n| `--quantification_method` | Quantification method for LFQ experiment | `feature_intensity` |\n| `--disable_table` | Disable protein/peptide table plots for large datasets | `False` |\n| `--ignored_idxml` | Ignore idXML files for faster processing | `False` |\n| `--parse_maxquant` | Generate reports based on MaxQuant results | `False` |\n| `--parse_proteobench` | Generate reports based on ProteoBench result | `False` |\n| `--mzid_plugin` | Generate reports based on mzIdentML files | `False` |\n\n## QC Metrics and Visualizations\n\npmultiqc generates a comprehensive report with multiple sections:\n\n### General Report\n\n- **Experimental Design**: Overview of the dataset structure\n- **Pipeline Performance Overview**: Key metrics including:\n - Contaminants Score\n - Peptide Intensity\n - Charge Score\n - Missed Cleavages\n - ID rate over RT\n - MS2 OverSampling\n - Peptide Missing Value\n- **Summary Table**: Spectra counts, identification rates, peptide and protein counts\n- **MS1 Information**: Quality metrics at MS1 level\n- **Pipeline Results Statistics**: Overall identification results\n- **Number of Peptides per Protein**: Distribution of peptide counts per protein\n\n### Results Tables\n\n- **Peptide Table**: First 500 peptides in the dataset\n- **PSM Table**: First 500 PSMs (Peptide-Spectrum Matches)\n\n### Identification Statistics\n\n- **Spectra Tracking**: Summary of identification results by file\n- **Search Engine Scores**: Distribution of search engine scores\n- **Precursor Charges Distribution**: Distribution of precursor ion charges\n- **Number of Peaks per MS/MS Spectrum**: Peak count distribution\n- **Peak Intensity Distribution**: MS2 peak intensity distribution\n- **Oversampling Distribution**: Analysis of MS2 oversampling\n- **Delta Mass**: Mass accuracy distribution\n- **Peptide/Protein Quantification Tables**: Quantitative levels across conditions\n\n## Example Reports\n\nYou can find example reports on the [docs page](https://bigbio.github.io/pmultiqc).\n\n## Development\n\nTo contribute to pmultiqc:\n\n1. 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