protrider 0.9.3

PROTRIDER - Protein outlier detection method

PROTRIDER

PROTRIDER is an autoencoder-based method to call protein outliers from mass spectrometry-based proteomics datasets.

Have a look at our paper for information about our work.

Table of Contents

• 🚀 Quickstart
• ⚙️ Installation
• 📖 Usage
  • 🗂️ Configuration
  • 📤 Output
  • ▶️ Run
• 📄 License
• 📚 Citation

🚀 Quickstart

# 1. Install
pip install protrider

# 2. Run on the included sample data
protrider run --config config.yaml

# 3. Plot results
protrider plot --config config.yaml all

Results are written to the directory specified by out_dir in config.yaml (default: output/). The key output file is protrider_summary.csv, which contains outlier calls with p-values, z-scores, and fold changes for every sample–protein pair.

⚙️ Installation

PROTRIDER was tested using Python 3.14 on Linux. We recommend a dedicated conda environment:

conda create --name protrider_env python=3.14
conda activate protrider_env
pip install protrider

Verify the installation:

protrider --help

More information on conda environments can be found in Conda's user guide.

📖 Usage

🗂️ Configuration

All parameters are set in a YAML configuration file. A template is provided as config.yaml.

Input files are specified in the config:

• input_intensities: CSV, TSV, or Parquet file with protein intensities (columns = samples, rows = proteins)
• sample_annotation (optional): CSV or TSV file with sample covariates (one row per sample)

An example dataset is included under sample_data/.

Configuration parameters

Parameter	Description
out_dir	Output directory
input_intensities	Path to protein intensities file
sample_annotation	Path to sample annotations file (optional)
index_col	Column name containing protein IDs
cov_used	List of covariate column names from the annotation file (optional)
find_q_method	Method to determine latent dimension: OHT (default), gs, bs (binary search), or an integer
pval_dist	Distribution for p-value calculation: t (default) or gaussian
n_epochs	Number of training epochs (default: 100)
checkpoint_path	Path to save/load model checkpoint (optional)

📤 Output

The key output file is protrider_summary.csv, which contains outlier calls with p-values, z-scores, and fold changes for every sample–protein pair.

Output files

File	Description
protrider_summary.csv	Long-format summary with outlier calls for all sample–protein pairs
pvals.csv	Two-sided p-values (samples × proteins)
pvals_adj.csv	BH/BY-adjusted p-values
pvals_one_sided.csv	Left-sided p-values
zscores.csv	Z-scores
residuals.csv	Model residuals (observed − predicted)
log2fc.csv	Log2 fold changes
fc.csv	Fold changes
output.csv	Autoencoder reconstructed values
processed_input.csv	Preprocessed input passed to the autoencoder
additional_info.csv	Model metadata (latent dimension, learning rate, loss)
train_losses.csv	Per-epoch training loss
fit_parameters.csv	Per-protein distribution fit parameters
config.yaml	Saved configuration for reproducibility

▶️ Run

Run the pipeline:

protrider run --config config.yaml

Generate plots:

# All plots
protrider plot --config config.yaml all

# Individual plot types
protrider plot --config config.yaml pvals
protrider plot --config config.yaml aberrant_per_sample
protrider plot --config config.yaml training_loss
protrider plot --config config.yaml encoding_dim

# Expected vs observed for a specific protein
protrider plot --config config.yaml expected_vs_observed --protein_id <protein_id>

Model checkpointing

PROTRIDER automatically saves trained models and reuses them in subsequent runs, skipping retraining if a checkpoint exists. By default the model is saved to <out_dir>/model.pt.

To use a custom checkpoint location, set checkpoint_path in your config:

checkpoint_path: models/my_model.pt

To force retraining, delete the checkpoint file or point to a new path.

Python API

import protrider

config = protrider.ProtriderConfig(
    out_dir='output/',
    input_intensities='data/protein_intensities.csv',
    sample_annotation='data/sample_annotations.csv',
    index_col='protein_ID',
    cov_used=['AGE', 'SEX'],
    n_epochs=100,
)

# Run
result, model_info, fit_params, gs_result = protrider.run(config)

# Save results
result.save(config.out_dir, format='wide')   # individual CSV files
result.save(config.out_dir, format='long')   # protrider_summary.csv
model_info.save(config.out_dir)
config.save(config.out_dir)

# Generate plots (omit out_dir to get plot objects without saving)
model_info.plot_training_loss(config.out_dir)
result.plot_aberrant_per_sample(config.out_dir)
hist_plot, qq_plot = result.plot_pvals(config.out_dir)
result.plot_expected_vs_observed('protein_123', config.out_dir)

# Access results as DataFrames
result.df_pvals        # p-values
result.df_pvals_adj    # adjusted p-values
result.df_Z            # z-scores
result.df_res          # residuals
result.log2fc          # log2 fold changes
result.fc              # fold changes

📄 License

This project is licensed under the MIT License.

📚 Citation

If you use PROTRIDER, please cite:

@article{10.1093/bioinformatics/btaf628,
    author = {Klaproth-Andrade, Daniela and Scheller, Ines F and Tsitsiridis, Georgios and Loipfinger, Stefan and Mertes, Christian and Smirnov, Dmitrii and Prokisch, Holger and Yépez, Vicente A and Gagneur, Julien},
    title = {PROTRIDER: Protein abundance outlier detection from mass spectrometry-based proteomics data with a conditional autoencoder},
    journal = {Bioinformatics},
    pages = {btaf628},
    year = {2025},
    month = {11},
    issn = {1367-4811},
    doi = {10.1093/bioinformatics/btaf628},
    url = {https://doi.org/10.1093/bioinformatics/btaf628},
}