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Ji, Y. ; Lotfollahi, M. ; Wolf, F.A. ; Theis, F.J.

Machine learning for perturbational single-cell omics.

Cell Syst. 12, 522-537 (2021)
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Cell biology is fundamentally limited in its ability to collect complete data on cellular phenotypes and the wide range of responses to perturbation. Areas such as computer vision and speech recognition have addressed this problem of characterizing unseen or unlabeled conditions with the combined advances of big data, deep learning, and computing resources in the past 5 years. Similarly, recent advances in machine learning approaches enabled by single-cell data start to address prediction tasks in perturbation response modeling. We first define objectives in learning perturbation response in single-cell omics; survey existing approaches, resources, and datasets (https://github.com/theislab/sc-pert); and discuss how a perturbation atlas can enable deep learning models to construct an informative perturbation latent space. We then examine future avenues toward more powerful and explainable modeling using deep neural networks, which enable the integration of disparate information sources and an understanding of heterogeneous, complex, and unseen systems.
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Publication type Article: Journal article
Document type Review
Corresponding Author
Keywords Cell State ; Deep Learning ; Drug ; Heterogeneous Systems ; Machine Learning ; Perturbation ; Single-cell; Rna-seq Data; Drug-sensitivity; Model; Prediction; Target; Identification; Heterogeneity; Combinations; Phenotypes; Signatures
ISSN (print) / ISBN 2405-4712
e-ISSN 2405-4720
Journal Cell Systems
Quellenangaben Volume: 12, Issue: 6, Pages: 522-537 Article Number: , Supplement: ,
Publisher Elsevier
Publishing Place Maryland Heights, MO
Non-patent literature Publications
Institute(s) Institute of Computational Biology (ICB)
Grants Helmholtz Association's Initiative and Networking Fund through sparse2big
Helmholtz Association's Initiative and Networking Fund through Helmholtz AI
BMBF
Silicon Valley Community Foundation
Chan Zuckerberg Initiative DAF