ArticleGenome biology2023
SCA: recovering single-cell heterogeneity through information-based dimensionality reduction.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- RAG: a regularized adaptive graph-based method for rare-cell identification from single-cell expression data.Briefings in bioinformatics · 2026Article
- Integrating host-microbiome multi-omics with machine learning: methods, benchmarks, and translational applications.Science China. Life sciences · 2026Review
- A systematic evaluation of highly variable gene selection methods for single-cell RNA-sequencing.Genome biology · 2025Article
- Label-free selection of marker genes in single-cell and spatial transcriptomics with geneCover.Genome research · 2025Article
- SurprisalAnalysis: an open-source software for information-theoretic analysis of gene expression.Bioinformatics advances · 2025Article
- scCAD: Cluster decomposition-based anomaly detection for rare cell identification in single-cell expression data.Nature communications · 2024Article
- Scanorama: integrating large and diverse single-cell transcriptomic datasets.Nature protocols · 2024Review
- LISA2: Learning Complex Single-Cell Trajectory and Expression Trends.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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.
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Registered trials
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