Predicting Single-cell Drug Sensitivity by Adaptive Weighted Feature for Adversarial Multi-source Domain Adaptation (2403.05260v1)
Abstract: The development of single-cell sequencing technology had promoted the generation of a large amount of single-cell transcriptional profiles, providing valuable opportunities to explore drug-resistant cell subpopulations in a tumor. However, the drug sensitivity data in single-cell level is still scarce to date, pressing an urgent and highly challenging task for computational prediction of the drug sensitivity to individual cells. This paper proposed scAdaDrug, a multi-source adaptive weighting model to predict single-cell drug sensitivity. We used an autoencoder to extract domain-invariant features related to drug sensitivity from multiple source domains by exploiting adversarial domain adaptation. Especially, we introduced an adaptive weight generator to produce importance-aware and mutual independent weights, which could adaptively modulate the embedding of each sample in dimension-level for both source and target domains. Extensive experimental results showed that our model achieved state-of-the-art performance in predicting drug sensitivity on sinle-cell datasets, as well as on cell line and patient datasets.
- A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening. Nature Machine Intelligence, 4(10):879–892, 2022.
- Domain separation networks. Advances in neural information processing systems, 29, 2016.
- Gene expression based inference of cancer drug sensitivity. Nature communications, 13(1):5680, 2022.
- Fader networks: Manipulating images by sliding attributes. Advances in neural information processing systems, 30, 2017.
- Predicting cellular responses to complex perturbations in high-throughput screens. Molecular Systems Biology, page e11517, 2023.
- Predicting cellular responses to novel drug perturbations at a single-cell resolution. Advances in Neural Information Processing Systems, 35:26711–26722, 2022.
- A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to covid-19 drug repurposing. Nature machine intelligence, 3(3):247–257, 2021.
- Gears: Predicting transcriptional outcomes of novel multi-gene perturbations. BioRxiv, pages 2022–07, 2022.
- Deep transfer learning of cancer drug responses by integrating bulk and single-cell rna-seq data. Nature Communications, 13(1):6494, 2022.
- A kernel two-sample test. The Journal of Machine Learning Research, 13(1):723–773, 2012.
- Enabling single-cell drug response annotations from bulk rna-seq using scad. Advanced Science, page 2204113, 2023.
- Genomics of drug sensitivity in cancer (gdsc): a resource for therapeutic biomarker discovery in cancer cells. Nucleic acids research, 41(D1):D955–D961, 2012.
- A survey of multi-source domain adaptation. Information Fusion, 24:84–92, 2015.
- A two-stage weighting framework for multi-source domain adaptation. Advances in neural information processing systems, 24, 2011.
- Exploiting web images for event recognition in consumer videos: A multiple source domain adaptation approach. In 2012 IEEE Conference on computer vision and pattern recognition, pages 1338–1345. IEEE, 2012.
- An empirical analysis of domain adaptation algorithms for genomic sequence analysis. Advances in neural information processing systems, 21, 2008.
- Multi-source distilling domain adaptation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 12975–12983, 2020.
- Multi-adversarial domain adaptation. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018.
- Partial feature selection and alignment for multi-source domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16654–16663, 2021.
- Learning interpretable cellular responses to complex perturbations in high-throughput screens. BioRxiv, pages 2021–04, 2021.
- The cancer genome atlas: creating lasting value beyond its data. Cell, 173(2):283–285, 2018.