sevml 0.3.0

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sEVML: Small Extracellular Vesicles Machine Learning Toolkit

A Python toolkit developed by the INSERM U1231 HSPpathies team for the analysis of ELISA-based datasets from small extracellular vesicles (sEVs).

Overview

sEVML is designed to facilitate the preprocessing, modeling, evaluation, and interpretation of machine learning pipelines based on ELISA data. It is optimized for the classification of biomarkers measured from sEVs.

Key features:

• Preprocess ELISA datasets (pivot, clean, normalize)
• Train XGBoost models with hyperparameter tuning
• Visualize learning and validation curves
• Evaluate model performance with ROC curves and confusion matrices
• Interpret feature importance using SHAP values

Installation

To install required dependencies:

pip install -r requirements.txt

Dependencies include:

• numpy
• pandas
• matplotlib
• scikit-learn
• xgboost
• shap

Usage

from sevml import (
    preprocess_elisa_dataset,
    train_xgb_with_gridsearch,
    evaluate_model,
    plot_model_curves,
    plot_shap_explanations
)

# Load and preprocess dataset
label_mapping = {"S": 0, "PD": 1}
X_train, X_test, y_train, y_test = preprocess_elisa_dataset("path/to/data.csv", label_mapping)

# Train model
model, params = train_xgb_with_gridsearch(X_train, y_train)

# Evaluate model
evaluate_model(model, X_train, y_train, X_test, y_test)

# Visualize curves
plot_model_curves(model, X_train, y_train)

# SHAP explanations
plot_shap_explanations(X_test, model, df_features)

API Reference

preprocess_elisa_dataset(filepath, label_mapping, test_size=0.2, random_state=5)

• Loads a raw ELISA CSV dataset.
• Pivots biomarker data to wide format.
• Imputes missing values (median).
• Scales features using MinMax.
• Splits data into training and testing sets.

train_xgb_with_gridsearch(X, y, eval_metric='logloss', random_state=5, cv=3)

• Trains an XGBoost classifier.
• Performs grid search over hyperparameters.
• Returns best model and parameters.

evaluate_model(model, X_train, y_train, X_test, y_test)

• Plots ROC curves and confusion matrices.
• Computes AUC, accuracy, and F1 scores.

plot_model_curves(...)

• Plots learning and validation curves.
• Helpful for diagnosing under/overfitting.

plot_shap_explanations(X, model, df_features)

• Uses SHAP to explain model predictions.
• Generates multiple plots: heatmap, violin, beeswarm, bar, waterfall.

Context

This package is developed and maintained by the HSPpathies team within the INSERM U1231 research unit. It is used in clinical and translational studies focusing on:

• Small extracellular vesicles (sEVs)
• Biomarkers of neurological and rare diseases
• Circulating PD-L1 analysis

License

MIT License

Authors

• Naïkem Isen
• HSPpathies Team, INSERM U1231

Contact

For questions or collaborations: [e-mail]