SCA: recovering single-cell heterogeneity through information-based dimensionality reduction
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13059_2023_Article_2998.pdf
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3.53 MB
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30188e1c6f7d15a2eefeedea8caf1d8d
Author(s)
DeMeo, Benjamin
Berger, Bonnie
Date Issued
August 25, 2023
Publisher
BioMed Central
Citation
Genome Biology. 2023 Aug 25;24(1):195
Version
Final published version
Abstract
Abstract
Dimensionality reduction summarizes the complex transcriptomic landscape of single-cell datasets for downstream analyses. Current approaches favor large cellular populations defined by many genes, at the expense of smaller and more subtly defined populations. Here, we present surprisal component analysis (SCA), a technique that newly leverages the information-theoretic notion of surprisal for dimensionality reduction to promote more meaningful signal extraction. For example, SCA uncovers clinically important cytotoxic T-cell subpopulations that are indistinguishable using existing pipelines. We also demonstrate that SCA substantially improves downstream imputation. SCA’s efficient information-theoretic paradigm has broad applications to the study of complex biological tissues in health and disease.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Mathematics
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1186/s13059-023-02998-7
