ArticleNature biotechnology2019
Efficient integration of heterogeneous single-cell transcriptomes using Scanorama.
Article in Nature biotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 520 papers, 4 of them syntheses that pooled it.
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Who cites it
520 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Single-cell RNA sequencing offers novel perspectives in viral infection research.Frontiers in cellular and infection microbiology · 2026Pooled it
- Contribution of germline and somatic mutations to risk of neuromyelitis optica spectrum disorder.Cell genomics · 2025Pooled it
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Meta-analyses of mouse and human prostate single-cell transcriptomes reveal widespread epithelial plasticity in tissue regression, regeneration, and cancer.Genome medicine · 2025Pooled it
- Immune-featured stromal niches associate with response to neoadjuvant immunotherapy in oral squamous cell carcinoma.Cell reports. Medicine · 2025Trial
- CellDiffusion: Enhancing scRNA-seq data integration with accurate inference of cell-cell association.Science advances · 2026Article
- Carmn sustains basal stemness in exocrine glands and CTND1-mediated Wnt signaling in mammary tissue.EMBO reports · 2026Article
- CRISPR-Enabled functional genomics in hPSCs-derived neural models for autism spectrum disorder.Metabolic brain disease · 2026Review
- Systematic benchmarking and optimal strategy selection of cross-species integration methods.Briefings in bioinformatics · 2026Article
- BRIDGE-KO: auditable transfer learning with bridged knockoffs improves cross-cohort microbiome prediction of immunotherapy response.Briefings in bioinformatics · 2026Article
- Concerted changes in the pediatric single-cell intestinal ecosystem before and after anti-TNF blockade.eLife · 2026Article
- COBRA: cell-type-specific orthogonal batch effect removal algorithm in single cell RNA-sequencing data.Bioinformatics (Oxford, England) · 2026Article
- DBP: Adaptive and Interpretable Factor Analysis for Single-cell RNA-seq Data with Deep Beta Processes.Genomics, proteomics & bioinformatics · 2026Article
- STAID: A Self-Refining Deep Learning Framework for Spatial Cell-Type Deconvolution with Biologically Informed Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- SKIM: A fast sketching strategy integrated with model's dynamic-feedback for large-scale single-cell transcriptomic analysis.PLoS computational biology · 2026Article
- Unify learns cellular evolution with universal multimodal embeddings.Nature communications · 2026Article
- Cross-species integration of single-cell data reveals conserved pathology-associated cell populations across animal models and human samples.Cell reports methods · 2026Article
- Spatial multi-omics imputation and embedding with SpaMIE.Cell reports methods · 2026Article
- SC-framework: A robust and FAIR semi-interactive environment for single-cell resolution datasets.iScience · 2026Article
- CellFuse Enables Multimodal Integration of Single-Cell and Spatial Proteomics Data for Systems-Level Analysis in Cancer.Cancer research · 2026Article
460 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
Integration of single-cell RNA sequencing (scRNA-seq) data from multiple experiments, laboratories and technologies can uncover biological insights, but current methods for scRNA-seq data integration are limited by a requirement for datasets to derive from functionally similar cells. We present Scanorama, an algorithm that identifies and merges the shared cell types among all pairs of datasets and accurately integrates heterogeneous collections of scRNA-seq data. We applied Scanorama to integrate and remove batch effects across 105,476 cells from 26 diverse scRNA-seq experiments representing 9 different technologies. Scanorama is sensitive to subtle temporal changes within the same cell lineage, successfully integrating functionally similar cells across time series data of CD14
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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.