Multi-domain translation between single-cell imaging and sequencing data using autoencoders.

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 di

SUBMITTER: Yang KD 

PROVIDER: S-EPMC7782789 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature