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

Abstract

Integration of single cell data modalities increases the richness of information about the heterogeneity of cell states, but integration of imaging and transcriptomics is an open challenge. Here the authors use autoencoders to learn a probabilistic coupling and map these modalities to a shared latent space.