ArticleBriefings in bioinformatics2021
iSMNN: batch effect correction for single-cell RNA-seq data via iterative supervised mutual nearest neighbor refinement.
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 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Single-cell RNA sequencing offers novel perspectives in viral infection research.Frontiers in cellular and infection microbiology · 2026Pooled it
- Gut microbiota and osteoarthritis: mechanisms and translation.Frontiers in immunology · 2026Review
- scBCN: deep learning-based batch correction network for integration of heterogeneous single-cell data.Briefings in bioinformatics · 2025Article
- Sceptic: pseudotime analysis for time-series single-cell sequencing and imaging data.Genome biology · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- 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
- 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
- Virus infection pattern imprinted and diversified the differentiation of T-cell memory in transcription and function.Frontiers in immunology · 2023Article
- Extensive proteome and functional genomic profiling of variability between genetically identical human B-lymphoblastoid cells.Scientific data · 2022Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Batch effect correction is an essential step in the integrative analysis of multiple single-cell RNA-sequencing (scRNA-seq) data. One state-of-the-art strategy for batch effect correction is via unsupervised or supervised detection of mutual nearest neighbors (MNNs). However, both types of methods only detect MNNs across batches of uncorrected data, where the large batch effects may affect the MNN search. To address this issue, we presented a batch effect correction approach via iterative supervised MNN (iSMNN) refinement across data after correction. Our benchmarking on both simulation and real datasets showed the advantages of the iterative refinement of MNNs on the performance of correction. Compared to popular alternative methods, our iSMNN is able to better mix the cells of the same cell type across batches. In addition, iSMNN can also facilitate the identification of differentially expressed genes (DEGs) that are relevant to the biological function of certain cell types. These results indicated that iSMNN will be a valuable method for integrating multiple scRNA-seq datasets that can facilitate biological and medical studies at single-cell level.
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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.