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.
CITATION STYLE
Singh, R., Hie, B. L., Narayan, A., & Berger, B. (2021). Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities. Genome Biology, 22(1). https://doi.org/10.1186/s13059-021-02313-2
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