Predicting single-cell perturbation responses for unseen drugsDownload PDF

31 Mar 2022, 13:06 (modified: 24 May 2022, 19:08)MLDD OralReaders: Everyone
Keywords: single cell, perturbation, disentanglement, transfer learning, drug discovery
TL;DR: We present a model which enables counterfactual response predictions for unseen drugs and evaluate how large scale bulk RNA-seq datasets can be leveraged to improve generalisation performance on single-cell data.
Abstract: 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.
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