biostudies-literatureDeMeo BMassachusetts Institute of TechnologyNHGRI NIH HHSNational Institutes of HealthNIGMS NIH HHSNIH HHS195https://www.ebi.ac.uk/biostudies/studies/S-EPMC10464206biostudies-literatureUnknown24(1)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 andGenome biologySCA: recovering single-cell heterogeneity through information-based dimensionality reduction.PMC10464206R01HG010959R35 GM1418611R35GM141861R35GM141861. AUTHOR INFORMATIONR01 HG010959Berger BDeMeo BfalseSCA: recovering single-cell heterogeneity through information-based dimensionality reduction.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 and2023-01-01T00:00:00Z2023 Aug2026-04-17T17:22:33.558Z2025-04-06T14:37:44.797ZS-EPMC104642063762641110.1186/s13059-023-02998-7