Our paper “Learning interpretable representations of single-cell multi-omics data with multi-output Gaussian processes” has been published in Nucleic Acids Research.

We present a unified framework that combines expressive neural embeddings with interpretable multi-output Gaussian processes for single-cell genomics. Joint representations of cells and genes reveal meaningful links between cell clusters and their marker genes via an interpretable gene-relevance map. Published in Nucleic Acids Research.