Predicting single-cell perturbation responses for unseen drugs

• URL: http://arxiv.org/abs/2204.13545v1
• Date: Thu, 28 Apr 2022 14:53:54 GMT
• Title: Predicting single-cell perturbation responses for unseen drugs
• Authors: Leon Hetzel, Simon B\"ohm, Niki Kilbertus, Stephan G\"unnemann, Mohammad Lotfollahi, Fabian Theis
• Abstract summary: We introduce a new encoder-decoder architecture to study the perturbational effects of unseen drugs. We envision that our proposed method will facilitate more efficient experiment designs through its ability to generate in-silico hypotheses.
• Score: 3.4591290171087747
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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