ArticleProceedings of the National Academy of Sciences of the United States of America2019
scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 108 papers, 1 of them a synthesis that pooled it.
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Who cites it
108 citing papers in PubMed, 1 synthesis or guideline pooled it.
- What are the applications of single-cell RNA sequencing in cancer research: a systematic review.Journal of experimental & clinical cancer research : CR · 2021Pooled it
- Moderated designs can balance between batch-effect mitigation and cell loss due to hashtag-assisted pooling in single-cell experiments.Genome research · 2026Article
- Analytical Methods and Application of Single-Cell and Single-Nucleus Transcriptomics in the Study of Ischemic Stroke.Biomolecules · 2026Review
- Neuronal subtype-specific ribosomal protein mRNA expression.RNA (New York, N.Y.) · 2026Article
- Cross-presentation of citrullinated antigens drives cytotoxic CD8bioRxiv : the preprint server for biology · 2026Article
- Dual platform spatial transcriptomics reveals parvalbumin interneuron subtype vulnerability in mouse models of Alzheimer's disease.Nature communications · 2026Article
- Cytotoxic T cell recognition of α-synuclein drives pathogenic immune responses in multiple system atrophy.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Cellular Diversity Underpins Cortical Organization and Disease Vulnerability in the Human Brain.Neuroscience bulletin · 2026Article
- Evaluating Batch Correction Methods for Large-Scale Mass Spectrometry Imaging of Heterogeneous Tissues.Analytical chemistry · 2026Article
- Calm in the midst of the storm: inferring gene expression stability in flowering plants.Frontiers in plant science · 2026Article
- Removal of unwanted variation in pseudobulk analysis of single-cell RNA sequencing data and the leveraging of pseudoreplicates.NAR genomics and bioinformatics · 2025Article
- Circulating IFNγ-associated protein signatures predict response to neoadjuvant immunotherapy in patients with stage III melanoma.Cancer cell international · 2025Article
- Benchmarking deep learning methods for biologically conserved single-cell integration.Genome biology · 2025Article
- Recovery of biological signals lost in single-cell batch integration with CellANOVA.Nature biotechnology · 2025Article
- 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
- Algebraic Approach to Maximum Likelihood Factor Analysis.Psychometrika · 2025Article
- Left ventricular myocardial molecular profile of human diabetic ischaemic cardiomyopathy.EMBO molecular medicine · 2025Article
- Spatial mutual nearest neighbors for spatial transcriptomics data.Bioinformatics (Oxford, England) · 2025Article
- Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses.Quantitative biology (Beijing, China) · 2025Review
- Integrating single-cell data with biological variables.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
48 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Concerted examination of multiple collections of single-cell RNA sequencing (RNA-seq) data promises further biological insights that cannot be uncovered with individual datasets. Here we present scMerge, an algorithm that integrates multiple single-cell RNA-seq datasets using factor analysis of stably expressed genes and pseudoreplicates across datasets. Using a large collection of public datasets, we benchmark scMerge against published methods and demonstrate that it consistently provides improved cell type separation by removing unwanted factors; scMerge can also enhance biological discovery through robust data integration, which we show through the inference of development trajectory in a liver dataset collection.
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