Multi-domain translation between single-cell imaging and sequencing data using autoencoders
Name
s41467-020-20249-2.pdf
Description
Published version
Size
2.63 MB
Format
Adobe PDF
Checksum (MD5)
99370d1eae1e90b922b3b20dbfe3d23c
Author(s)
Yang, Karren Dai
Belyaeva, Anastasiya
Venkatachalapathy, Saradha
Damodaran, Karthik
Katcoff, Abigail
Radhakrishnan, Adityanarayanan
Shivashankar, GV
Uhler, Caroline
Date Issued
2021
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Version
Final published version
Abstract
© 2021, The Author(s). 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.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41467-020-20249-2
