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cff-version: 1.2.0 |
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title: >- |
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Predicting Cellular Responses to Novel Drug |
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Perturbations at a Single-Cell Resolution |
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message: >- |
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If you use this software, please cite it using the |
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metadata from this file. |
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type: software |
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authors: |
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- given-names: Leon |
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family-names: Hetzel |
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- given-names: Simon |
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family-names: Boehm |
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- given-names: Niki |
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family-names: Kilbertus |
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- given-names: Stephan |
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family-names: Günnemann |
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- given-names: Mohammad |
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family-names: Lotfollahi |
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- given-names: Fabian |
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name-particle: J |
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family-names: Theis |
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identifiers: |
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- type: url |
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value: 'https://neurips.cc/virtual/2022/poster/53227' |
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repository-code: 'https://github.com/theislab/chemCPA' |
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abstract: >+ |
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Single-cell transcriptomics enabled the study of |
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cellular heterogeneity in response to perturbations |
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at the resolution of individual cells. However, |
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scaling high-throughput screens (HTSs) to measure |
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cellular responses for many drugs remains a |
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challenge due to technical limitations and, more |
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importantly, the cost of such multiplexed |
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experiments. Thus, transferring information from |
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routinely performed bulk RNA HTS is required to |
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enrich single-cell data meaningfully.We introduce |
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chemCPA, a new encoder-decoder architecture to |
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study the perturbational effects of unseen drugs. |
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We combine the model with an architecture surgery |
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for transfer learning and demonstrate how training |
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on existing bulk RNA HTS datasets can improve |
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generalisation performance. Better generalisation |
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reduces the need for extensive and costly screens |
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at single-cell resolution. We envision that our |
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proposed method will facilitate more efficient |
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experiment designs through its ability to generate |
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in-silico hypotheses, ultimately accelerating drug |
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discovery. |
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keywords: |
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- transfer learning |
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- disentanglement |
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- perturbation |
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- single cell |
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- genomics |
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- Drug Discovery |
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- unsupervised |
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