ArticleNature biotechnology2025
Recovery of biological signals lost in single-cell batch integration with CellANOVA.
Article in Nature biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Moderated designs can balance between batch-effect mitigation and cell loss due to hashtag-assisted pooling in single-cell experiments.Genome research · 2026Article
- Single-cell-level perturbation-induced and condition-related signal estimation with batch effect removal using NDreamer.Briefings in bioinformatics · 2026Article
- IntegrateRigor: annotation-free integration optimization for cell identity recovery reveals cancer-immune interface niches.bioRxiv : the preprint server for biology · 2026Article
- Condition-Associated Pattern Extraction and Recovery From Multi-Condition Single-Cell RNA-seq Data With CAPER.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Spatiotemporal analysis of autism gene enrichment implicates cortex, thalamus, and hypothalamus.bioRxiv : the preprint server for biology · 2026Article
- Integrating and mapping single-cell transcriptomics across the entire gene expression space.Briefings in bioinformatics · 2026Article
- Revealing a coherent cell-state landscape across single-cell datasets with CONCORD.Nature biotechnology · 2026Article
- Design, processing, and modeling for longitudinal multiomics microbiome data.Frontiers in cellular and infection microbiology · 2026Review
- Toward trustworthy virtual cells: a roadmap for perturbation-resolved, context-aware, and experimentally validated cell models.Frontiers in cell and developmental biology · 2026Review
- multiVIB: A unified probabilistic contrastive learning framework for atlas-scale integration of single-cell multi-omics data.bioRxiv : the preprint server for biology · 2025Article
- Joint profiling of gene expression and chromatin accessibility in pancreatic lymph nodes and spleens in human type 1 diabetes.Science immunology · 2025Article
- Single-Cell Multi-Omics in Type 2 Diabetes Mellitus: Revealing Cellular Heterogeneity and Mechanistic Insights.International journal of molecular sciences · 2025Review
- Moderated designs can balance between batch-effect mitigation and cell loss due to hashtag-assisted pooling in single-cell experiments.bioRxiv : the preprint server for biology · 2025Article
- Revealing a coherent cell state landscape across single cell datasets with CONCORD.bioRxiv : the preprint server for biology · 2025Article
- Integrative, high-resolution analysis of single-cell gene expression across experimental conditions with PARAFAC2-RISE.Cell systems · 2025Article
- Article
- A single-cell transcriptomic atlas of developing inhibitory neurons reveals expanding and contracting modes of diversification.bioRxiv : the preprint server for biology · 2025Article
- scLTNN: an innovative tool for automatically visualizing single-cell trajectories.Bioinformatics advances · 2025Article
- Semisynthetic simulation for microbiome data analysis.Briefings in bioinformatics · 2024Review
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11 authors.
Funding
Abstract
Data integration to align cells across batches has become a cornerstone of single-cell data analysis, critically affecting downstream results. Currently, there are no guidelines for when the biological differences between samples are separable from batch effects. Here we show that current paradigms for single-cell data integration remove biologically meaningful variation and introduce distortion. We present a statistical model and computationally scalable algorithm, CellANOVA (cell state space analysis of variance), that harnesses experimental design to explicitly recover biological signals that are erased during single-cell data integration. CellANOVA uses a 'pool-of-controls' design concept, applicable across diverse settings, to separate unwanted variation from biological variation of interest and allow the recovery of subtle biological signals. We apply CellANOVA to diverse contexts and validate the recovered biological signals by orthogonal assays. In particular, we show that CellANOVA is effective in the challenging case of single-cell and single-nucleus data integration, where it recovers subtle biological signals that can be validated and replicated by external 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.