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title: >-
Predicting Cellular Responses to Novel Drug
Perturbations at a Single-Cell Resolution
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Leon
family-names: Hetzel
- given-names: Simon
family-names: Boehm
- given-names: Niki
family-names: Kilbertus
- given-names: Stephan
family-names: Günnemann
- given-names: Mohammad
family-names: Lotfollahi
- given-names: Fabian
name-particle: J
family-names: Theis
identifiers:
- type: url
value: 'https://neurips.cc/virtual/2022/poster/53227'
repository-code: 'https://github.com/theislab/chemCPA'
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.
keywords:
- transfer learning
- disentanglement
- perturbation
- single cell
- genomics
- Drug Discovery
- unsupervised
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