PREDICTING SINGLE-CELL PERTURBATION RESPONSES FOR UNSEEN DRUGS

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Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells. However, scaling high-throughput screens (HTSs) to measure cellular responses for many drugs remains a challenge due to technical limitations and, more importantly, the cost of such multiplexed experiments. Thus, transferring information from routinely performed bulk RNA-seq HTS is required to enrich single-cell data meaningfully. We introduce a new encoder-decoder architecture to study the perturbational effects of unseen drugs. We combine the model with a transfer learning scheme and demonstrate how training on existing bulk RNA-seq HTS datasets can improve generalisation performance. Better generalisation reduces the need for extensive and costly screens at single-cell resolution. We envision that our proposed method will facilitate more efficient experiment designs through its ability to generate in-silico hypotheses, ultimately accelerating targeted drug discovery.

Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution Leon Hetzel∗1, 3 , Simon Böhm∗3 , Niki Kilbertus2, 4 , Stephan Günnemann2 , Mohammad Lotfollahi1, 5 , and Fabian Theis1, 3 arXiv:2204.13545v2 [cs.LG] 30 Dec 2022 {leon.hetzel, simon.boehm, niki.kilbertus}@helmholtz-muenchen.de…

PREDICTING SINGLE-CELL PERTURBATION RESPONSES FOR UNSEEN DRUGS

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