ArticleNature biotechnology2024
Characterizing the impacts of dataset imbalance on single-cell data integration.
Article in Nature biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- SKIM: A fast sketching strategy integrated with model's dynamic-feedback for large-scale single-cell transcriptomic analysis.PLoS computational biology · 2026Article
- IntegrateRigor: annotation-free integration optimization for cell identity recovery reveals cancer-immune interface niches.bioRxiv : the preprint server for biology · 2026Article
- ProtoCloud: A prototypical self-explaining model for single-cell analysis.Cell genomics · 2026Article
- Shortcomings of silhouette in single-cell integration benchmarking.Nature biotechnology · 2026Article
- From Epigenetic Constraint to Evolutionary Escape: Cell-State Transitions and Selective Pressures During Malignant Transformation in Lower-Grade Gliomas.Biomedicines · 2026Review
- Co-occurrence of transcriptionally distinct persister cell states underpins neoadjuvant therapy resistance in triple‑negative breast cancer.Genome medicine · 2026Article
- Representation learning of single-cell RNA-seq data.RNA (New York, N.Y.) · 2026Review
- Multi-view deep learning of highly multiplexed imaging data improves association of cell states with clinical outcomes.Bioinformatics advances · 2026Article
- Toward trustworthy virtual cells: a roadmap for perturbation-resolved, context-aware, and experimentally validated cell models.Frontiers in cell and developmental biology · 2026Review
- Multimodal landscape of atherosclerotic plaques: A spatial omics approach with mass spectrometry imaging.Analytica chimica acta · 2025Review
- scCotag: Diagonal integration of single-cell multi-omics data via prior-informed co-optimal transport and regularized barycentric mapping.bioRxiv : the preprint server for biology · 2025Article
- Single-cell RNA sequencing reveals genes relevant to periodontal therapy and periodontitis.Physiological genomics · 2025Article
- Coralysis enables sensitive identification of imbalanced cell types and states in single-cell data via multi-level integration.Nucleic acids research · 2025Article
- Adaptive resampling for improved machine learning in imbalanced single-cell datasets.bioRxiv : the preprint server for biology · 2025Article
- Spatial Dependence and Heterogeneity in Molecular Imaging: Moran Quadrant Maps Enable Advanced Spatial-Statistical Analysis.bioRxiv : the preprint server for biology · 2025Article
- Insights, opportunities, and challenges provided by large cell atlases.Genome biology · 2025Review
- On metrics for subpopulation detection in single-cell and spatial omics data.Nucleic acids research · 2025Article
- High performance data integration for large-scale analyses of incomplete Omic profiles using Batch-Effect Reduction Trees (BERT).Nature communications · 2025Article
- Analysis of individual patient pathway coordination in a cross-species single-cell kidney atlas.Nature genetics · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Computational methods for integrating single-cell transcriptomic data from multiple samples and conditions do not generally account for imbalances in the cell types measured in different datasets. In this study, we examined how differences in the cell types present, the number of cells per cell type and the cell type proportions across samples affect downstream analyses after integration. The Iniquitate pipeline assesses the robustness of integration results after perturbing the degree of imbalance between datasets. Benchmarking of five state-of-the-art single-cell RNA sequencing integration techniques in 2,600 integration experiments indicates that sample imbalance has substantial impacts on downstream analyses and the biological interpretation of integration results. Imbalance perturbation led to statistically significant variation in unsupervised clustering, cell type classification, differential expression and marker gene annotation, query-to-reference mapping and trajectory inference. We quantified the impacts of imbalance through newly introduced properties-aggregate cell type support and minimum cell type center distance. To better characterize and mitigate impacts of imbalance, we introduce balanced clustering metrics and imbalanced integration guidelines for integration method users.
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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.