A baseline ML model to predict cell responses to drug perturbations
Award-winning solution from the NeurIPS 2023 Single-Cell Perturbations Challenge:
- 🥇 $10,000 Judges' Prize for performance and methodology
- 🥈 2nd place in post-hoc analysis
- 📊 Top 2% overall (16th/1097 teams)
pip install git+https://github.com/scapeML/scape.git
import scape # data from zenodo can be downloaded via scape.io.download_from_zenodo(target_dir = ".") # Train model with drug cross-validation result = scape.api.train( de_file="_data/de_train.parquet", lfc_file="_data/lfc_train.parquet", cv_drug="Belinostat", n_genes=64 ) # Visualize performance vs baselines scape.util.plot_result(result._last_train_results)
ScAPE is a lightweight neural network (~9.6M parameters for the single-task version) that predicts differential gene expression in response to drug perturbations. Built with Keras 3 for multi-backend support (TensorFlow, JAX, PyTorch).
- 🎯 Single or Multi-Task Learning: Predict p-values only or jointly with fold changes
- 🔄 Multi-Backend Support: Choose between TensorFlow, JAX, or PyTorch
- 🎲 Built-in Ensemble Methods: Simple blending for robust predictions
- 📊 Cross-Validation: Cell-type and drug-based validation strategies
- ⚡ Efficient: Handles ~18,000 genes with median-based feature engineering
The model uses median-based feature engineering: for each drug and cell type, we compute median differential expression values across the dataset. This reduces ~18,000 genes to manageable drug/cell signatures while preserving biological signal.
# Command line python -m scape train --n-genes 64 --cv-drug Belinostat _data/de_train.parquet _data/lfc_train.parquet # Python API import scape model = scape.model.create_default_model( n_genes=64, df_de=de_data, df_lfc=lfc_data ) results = model.train( val_cells=['NK cells'], val_drugs=['Belinostat'], epochs=600 )
Configure the model to jointly predict both p-values and fold changes:
# Multi-task configuration with optimal weights model.model.compile( optimizer=optimizer, loss={'slogpval': mrrmse, 'lfc': mrrmse}, loss_weights={'slogpval': 0.8, 'lfc': 0.2} )
# Use JAX backend (recommended for performance) KERAS_BACKEND=jax python -m scape train ... # Use TensorFlow backend KERAS_BACKEND=tensorflow python -m scape train ... # Use PyTorch backend KERAS_BACKEND=torch python -m scape train ...
Improve robustness with simple ensemble blending:
from sklearn.model_selection import KFold import numpy as np # Train multiple models with K-fold predictions = [] for train_idx, val_idx in KFold(n_splits=5).split(all_combinations): model = scape.model.create_default_model(...) model.train(...) predictions.append(model.predict(test_combinations)) # Blend predictions (median) ensemble_pred = np.median([p.values for p in predictions], axis=0)
# Custom architecture config = { "encoder_hidden_layer_sizes": [128, 128], "decoder_hidden_layer_sizes": [128, 512], "outputs": { "slogpval": (64, "linear"), "lfc": (64, "linear"), # Multi-task }, "noise": 0.01, "dropout": 0.05 } model = scape.model.create_model( n_genes=64, df_de=de_data, df_lfc=lfc_data, config=config )
Track model improvement over baselines:
- Zero baseline: Always predicts 0 (competition baseline)
- Median baseline: Predicts drug-specific medians
- 📓 Quick Start Tutorial
- 📓 Training Pipeline
- 📓 Google Colab Demo
- 📄 Technical Report
- 💾 Dataset (Zenodo)
# Setup with pixi pixi install pixi shell -e dev # Run tests (JAX backend recommended) KERAS_BACKEND=jax pixi run -e dev test # Lint & format pixi run lint pixi run format
@article {scape2025perturb, author = {Romero-Tapiador, Sergio and Rodriguez-Mier, Pablo and Garrido-Rodriguez, Martin and Tolosana, Ruben and Morales, Aythami and Saez-Rodriguez, Julio}, title = {ScAPE: A lightweight multitask learning baseline method to predict transcriptomic responses to perturbations}, year = {2025}, doi = {10.1101/2025.09.08.674873}, publisher = {Cold Spring Harbor Laboratory}, URL = {https://www.biorxiv.org/content/early/2025/09/09/2025.09.08.674873}, eprint = {https://www.biorxiv.org/content/early/2025/09/09/2025.09.08.674873.full.pdf}, journal = {bioRxiv} }