Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities
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13059_2021_Article_2313.pdf
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Author(s)
Singh, Rohit
Hie, Brian L.
Narayan, Ashwin
Berger, Bonnie
Date Issued
May 2021
Journal
Genome Biology
Publisher
BioMed Central
Citation
Genome Biology. 2021 May 03;22(1):131
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1186/s13059-021-02313-2
