Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities

A complete understanding of biological processes requires synthesizing information across heterogeneous modalities, such as age, disease status, or gene expression. Technological advances in single-cell profiling have enabled researchers to assay multiple modalities simultaneously. We present Schema, which uses a principled metric learning strategy that identifies informative features in a modality to synthesize disparate modalities into a single coherent interpretation. We use Schema to infer cell types by integrating gene expression and chromatin accessibility data; demonstrate informative data visualizations that synthesize multiple modalities; perform differential gene expression analysis in the context of spatial variability; and estimate evolutionary pressure on peptide sequences.

Automated summary

Schema is a tool that synthesizes multiple biological information modalities using a metric learning strategy, which can be used for inferring cell types, data visualization, performing differential gene expression analysis, and estimating evolutionary pressure on peptide sequences.