Single-cell Afatinib Response of Triple Negative Cells
a pipeline for triple negative single cell data afatinib drug response prediction. It includes a contrastive autoencoder, for dimensionality reduction and feature extraction and a svm for the classification
pipeline is provided as a pickled scikit-learn compatible estimator to be easily integrated seamlessly in any python workflow. It can be used with the following methods:
- predict(X): predict the class of cell ( 1 means resistent)
- transform(X): get embedding of input vector
- predict_proba(X): get probability of each class
- data
- afatinib.csv: afatinib drug response data
- signature: gene signature
- train_set: the preprocessed mdamb468 labeled cell line (only 374 genes)
- cell line: contains preprocessed validation data
- preprocessing script.py: script to preprocess data before giving in input to scASTRAL
- models
- scASTRAL_pipeline.sk: scikit-learn compatible estimator for scastral classification and feature extraction
- scastral
- network.py: torch modules for scastral
- utils.py: utilities for loading and filtering data
- preprocessing.py: scikit-learn compatible Transformers for count normalization
- train_model.ipynb: jupyter notebook illustrating model training
- validate_model.ipynb: jupyter model illustrating model validation
- find_treshold.py: process to estimate confidence thresholds