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Title: Multi-domain translation between single-cell imaging and sequencing data using autoencoders
Authors: Yang, Karren Dai
Belyaeva, Anastasiya
Venkatachalapathy, Saradha 
Damodaran, Karthik 
Katcoff, Abigail
Radhakrishnan, Adityanarayanan
Shivashankar, G., V 
Uhler, Caroline
Issue Date: 4-Jan-2021
Publisher: Nature Research
Citation: Yang, Karren Dai, Belyaeva, Anastasiya, Venkatachalapathy, Saradha, Damodaran, Karthik, Katcoff, Abigail, Radhakrishnan, Adityanarayanan, Shivashankar, G., V, Uhler, Caroline (2021-01-04). Multi-domain translation between single-cell imaging and sequencing data using autoencoders. Nature Communications 12 (1) : 31. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
Abstract: The development of single-cell methods for capturing different data modalities including imaging and sequencing has revolutionized our ability to identify heterogeneous cell states. Different data modalities provide different perspectives on a population of cells, and their integration is critical for studying cellular heterogeneity and its function. While various methods have been proposed to integrate different sequencing data modalities, coupling imaging and sequencing has been an open challenge. We here present an approach for integrating vastly different modalities by learning a probabilistic coupling between the different data modalities using autoencoders to map to a shared latent space. We validate this approach by integrating single-cell RNA-seq and chromatin images to identify distinct subpopulations of human naive CD4+ T-cells that are poised for activation. Collectively, our approach provides a framework to integrate and translate between data modalities that cannot yet be measured within the same cell for diverse applications in biomedical discovery. © 2021, The Author(s).
Source Title: Nature Communications
ISSN: 2041-1723
DOI: 10.1038/s41467-020-20249-2
Rights: Attribution 4.0 International
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