ArticleBriefings in bioinformatics2021
SMNN: batch effect correction for single-cell RNA-seq data via supervised mutual nearest neighbor detection.
Article in Briefings in bioinformatics, 2021. 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed.
- Inflammasomes meet organoids and artificial intelligence: unraveling the complexity of gynecological inflammation.Frontiers in immunology · 2026Review
- Sceptic: pseudotime analysis for time-series single-cell sequencing and imaging data.Genome biology · 2025Article
- HiCat: a semi-supervised approach for cell type annotation.Briefings in bioinformatics · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Detecting anomalous anatomic regions in spatial transcriptomics with STANDS.Nature communications · 2024Article
- Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Data normalization for addressing the challenges in the analysis of single-cell transcriptomic datasets.BMC genomics · 2024Review
- Dynamics of alternative polyadenylation in single root cells ofFrontiers in plant science · 2024Article
- Domain adaptation for supervised integration of scRNA-seq data.Communications biology · 2023Article
- CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity.Bioinformatics (Oxford, England) · 2023Article
- Batch alignment of single-cell transcriptomics data using deep metric learning.Nature communications · 2023Article
- SCIBER: a simple method for removing batch effects from single-cell RNA-sequencing data.Bioinformatics (Oxford, England) · 2023Article
- A comprehensive comparison of supervised and unsupervised methods for cell type identification in single-cell RNA-seq.Briefings in bioinformatics · 2022Article
- Integration of single cell data by disentangled representation learning.Nucleic acids research · 2022Article
- Progenitor translatome changes coordinated by Tsc1 increase perception of Wnt signals to end nephrogenesis.Nature communications · 2021Article
- iSMNN: batch effect correction for single-cell RNA-seq data via iterative supervised mutual nearest neighbor refinement.Briefings in bioinformatics · 2021Article
- Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology.International journal of molecular sciences · 2021Review
- Integration and transfer learning of single-cell transcriptomes via cFIT.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Computational methods for the integrative analysis of single-cell data.Briefings in bioinformatics · 2021Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Batch effect correction has been recognized to be indispensable when integrating single-cell RNA sequencing (scRNA-seq) data from multiple batches. State-of-the-art methods ignore single-cell cluster label information, but such information can improve the effectiveness of batch effect correction, particularly under realistic scenarios where biological differences are not orthogonal to batch effects. To address this issue, we propose SMNN for batch effect correction of scRNA-seq data via supervised mutual nearest neighbor detection. Our extensive evaluations in simulated and real datasets show that SMNN provides improved merging within the corresponding cell types across batches, leading to reduced differentiation across batches over MNN, Seurat v3 and LIGER. Furthermore, SMNN retains more cell-type-specific features, partially manifested by differentially expressed genes identified between cell types after SMNN correction being biologically more relevant, with precision improving by up to 841.0%.
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Registered trials
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.