Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities
Author(s)
Singh, Rohit; Hie, Brian L.; Narayan, Ashwin; Berger, Bonnie
Download13059_2021_Article_2313.pdf (1.977Mb)
Publisher with Creative Commons License
Publisher with Creative Commons License
Creative Commons Attribution
Terms of use
Metadata
Show full item recordAbstract
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.
Date issued
2021-05Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of MathematicsJournal
Genome Biology
Publisher
BioMed Central
Citation
Genome Biology. 2021 May 03;22(1):131
Version: Final published version
ISSN
1474-760X