Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution

MCML Authors

Leon Hetzel

→ Group Fabian Theis
 Mathematical Modelling of Biological Systems

Niki Kilbertus

Prof. Dr.

Principal Investigator

Ethics in Systems Design and Machine Learning

Stephan Günnemann

Prof. Dr.

Principal Investigator

Data Analytics & Machine Learning

Fabian Theis

Prof. Dr.

Principal Investigator

Mathematical Modelling of Biological Systems

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 HTS is required to enrich single-cell data meaningfully.We introduce chemCPA, a new encoder-decoder architecture to study the perturbational effects of unseen drugs. We combine the model with an architecture surgery for transfer learning and demonstrate how training on existing bulk RNA 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 drug discovery.

inproceedings

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NeurIPS 2022

36th Conference on Neural Information Processing Systems. New Orleans, LA, USA, Nov 28-Dec 09, 2022.

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Authors

L. Hetzel • S. Boehm • N. Kilbertus • S. Günnemann • M. Lotfollahi • F. J. Theis

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Links

URL

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Research Areas

 A3 | Computational Models

 C2 | Biology