ArticleNature genetics2025
CHOIR improves significance-based detection of cell types and states from single-cell data.
Article in Nature genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- scMustree: a multi-scale functional hierarchy for single-cell transcriptomic analysis.Bioinformatics (Oxford, England) · 2026Article
- Cell-type Plasticity Supports Behavioral Adaptations at the Water-to-Land Interface.bioRxiv : the preprint server for biology · 2026Article
- Review
- Autism Sensory Profiles Predict Stimulus- Evoked Insula Connectivity.medRxiv : the preprint server for health sciences · 2026Article
- Systematic clustering alignment and feature characterization for single-cell omics using ACE-OF-Clust.bioRxiv : the preprint server for biology · 2026Article
- ICON: An isoform-aware hierarchical random forest model for cell type classification.bioRxiv : the preprint server for biology · 2026Article
- Quantifying stability via count splitting to guide model selection in RNA velocity analyses.bioRxiv : the preprint server for biology · 2026Article
- Cell neighborhood topology directs rare cell population identification.Nature communications · 2026Article
- Quantifying stability via count splitting to guide model selection in RNA velocity analyses.Bioinformatics advances · 2026Article
- ThebioRxiv : the preprint server for biology · 2025Article
- Pan-Angiosperm Analysis of the CLE Signaling Peptide Gene Family Unveils Paths, Patterns, and Predictions of Paralog Diversification.Molecular biology and evolution · 2025Article
- Article
- Article
- CHOIR improves significance-based detection of cell types and states from single-cell data.Nature genetics · 2025Article
- Artificial variables help to avoid over-clustering in single-cell RNA sequencing.American journal of human genetics · 2025Article
- Review
- Single-nucleus RNA-seq reveals no increase in T cells in Alzheimer's disease prefrontal cortex or hippocampus.Frontiers in cellular neuroscience · 2025Article
- Widespread associations between behavioral metrics and brain microstructure in ASD suggest age mediates subtypes of ASD.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Synthetic control removes spurious discoveries from double dipping in single-cell and spatial transcriptomics data analyses.bioRxiv : the preprint server for biology · 2024Article
- CHAI: consensus clustering through similarity matrix integration for cell-type identification.Briefings in bioinformatics · 2024Article
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3 authors.
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Abstract
Clustering is a critical step in the analysis of single-cell data, enabling the discovery and characterization of cell types and states. However, most popular clustering tools do not subject results to statistical inference testing, leading to risks of overclustering or underclustering data and often resulting in ineffective identification of cell types with widely differing prevalence. To address these challenges, we present CHOIR (cluster hierarchy optimization by iterative random forests), which applies a framework of random forest classifiers and permutation tests across a hierarchical clustering tree to statistically determine clusters representing distinct populations. We demonstrate the performance of CHOIR through extensive benchmarking against 15 existing clustering methods across 230 simulated and five real single-cell RNA sequencing, assay for transposase-accessible chromatin sequencing, spatial transcriptomic and multi-omic datasets. CHOIR can be applied to any single-cell data type and provides a flexible, scalable and robust solution to the challenge of identifying biologically relevant cell groupings within heterogeneous single-cell data.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.