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| cff-version: 1.2.0 |
| 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. |
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| keywords: |
| - transfer learning |
| - disentanglement |
| - perturbation |
| - single cell |
| - genomics |
| - Drug Discovery |
| - unsupervised |
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