Abstract
Predicting the cell response to drugs is central to drug discovery, drug repurposing, and personalized medicine. To this end, large datasets of drug signatures have been curated, most notably the Connectivity Map (CMap). A multitude of in silico approaches have also been formulated, but strategies for predicting drug signatures in unseen cells—cell lines not in the reference datasets—are still lacking. In this work, we developed a simple-yet-efficacious computational strategy, called CrossTx, for predicting the drug transcriptomic signatures of an unseen target cell line using drug transcriptome data of reference cell lines and unlabeled transcriptome data of the target cells. Our strategy involves the combination of Predictor and Corrector steps. The Predictor generates cell-line-agnostic drug signatures using the reference dataset, while the Corrector produces target-cell-specific drug signatures by projecting the signatures from the Predictor onto the transcriptomic latent space of the target cell line. Testing different Predictor−Corrector functions using the CMap revealed the combination of averaging (Mean) as a Predictor and Principal Component Analysis (PCA) followed by Autoencoder (AE) as a Corrector to be the best. Yet, using Mean as a Predictor and PCA as a Corrector achieved comparatively high accuracy with much lower computational requirements when compared to the best combination.