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Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities
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13059_2021_Article_2313.pdf
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1.98 MB
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Adobe PDF
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bf03acdd314d42e96016979f932a4a7d
Author(s)
Singh, Rohit
Hie, Brian L.
Narayan, Ashwin
Berger, Bonnie
Date Issued
May 3, 2021
Publisher
BioMed Central
Citation
Genome Biology. 2021 May 03;22(1):131
Version
Final published version
Abstract
Abstract
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.
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1186/s13059-021-02313-2
