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Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Open accessPublished version of a preprint
Abstract
Large-scale single-cell RNA sequencing (scRNA-seq) data sets that are produced in different laboratories and at different times contain batch effects that may compromise the integration and interpretation of the data. Existing scRNA-seq analysis methods incorrectly assume that the composition of cell populations is either known or identical across batches. We present a strategy for batch correction based on the detection of mutual nearest neighbors (MNNs) in the high-dimensional expression space. Our approach does not rely on predefined or equal population compositions across batches; instead, it requires only that a subset of the population be shared between batches. We demonstrate the superiority of our approach compared with existing methods by using both simulated and real scRNA-seq data sets. Using multiple droplet-based scRNA-seq data sets, we demonstrate that our MNN batch-effect-correction method can be scaled to large numbers of cells.
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In the citing paper
“This analysis shows that the batch-mixing entropy is increased after background correction, indicating better mixing between samples (Fig. 4E).”
From SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data (2020) · cites this paper as [20]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“PCA dimensionality reduction and mutual-nearest neighbours84 or Harmony34 batch correction was performed to correct for non-biological confounders between replicates and timepoints.”
From Cell diversity and plasticity during atrioventricular heart valve EMTs (2023) · cites this paper as [84]
In the citing paper
“We confirm that using an in silico batch correction maintains the sensitivity of all DA methods, comparable to the case of no batch effect, across different batch effect magnitudes (Supp Fig 9; batch effects corrected using fastMNN [19]”
From Differential abundance testing on single-cell data using k-nearest neighbor graphs (2022) · cites this paper as [19]
In the citing paper
“We found that the SCING GRN module based dimensionality reduction carried the ability to correct for batch effect and retain biological information (Figure S6) in a similar manner to dedicated batch correction methods such as FastMNN,41 Harmony,42 and Seurat.43”
From SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics (2023) · cites this paper as [41]
In the citing paper
“the most widely used data alignment approaches (Butler et al., 2018; Haghverdi et al., 2018; Johnson et al., 2007; Risso et al., 2014) implicitly assume that the differences between datasets arise entirely from technical variation and attempt to eliminate them, or map datasets into a completely shared latent space using dimensions of maximum correlation (Butler et al., 2018).”
From Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity (2019) · cites this paper as Haghverdi et al., 2018
In the citing paper
“Data integration methods such as Canonical Correlation Analysis (CCA; Butler et al, 2018), Mutual Nearest Neighbours (MNN; Haghverdi et al, 2018), Scanorama (preprint: Hie et al, 2018), RISC (preprint: Liu et al, 2018), scGen (preprint: Lotfollahi et al, 2018), LIGER (preprint: Welch et al, 2018), BBKNN (preprint: Park et al, 2018), and Harmony (preprint: Korsunsky et al, 2018) have been developed”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Haghverdi et al, 2018
In the citing paper
“These methods are potentially affected by the batch effect across platforms and experiments 12 .”
From scBERT as a Large-scale Pretrained Deep Language Model for Cell Type Annotation of Single-cell RNA-seq Data (2021) · cites this paper as [12]
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Metadata
- Authors
- Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C.
- Affiliations
- European Bioinformatics Institute (GB)
- University of Cambridge (GB)
- Wellcome Sanger Institute (GB)
- Corresponding author
- Marioni, John C
- Venue
- Nature Biotechnology
- Published
- 2018-04-02
- Volume
- 36
- Issue
- 5
- Pages
- 421-427
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC6152897
- OpenAlex
- W2794521141
- Cited by
- 2,416
- References
- 33
- Citations by year
- 2026: 266 · 2025: 355 · 2024: 387 · 2023: 364 · 2022: 271 · 2021: 321 · 2020: 240 · 2019: 144 · 2018: 63
- FWCI
- 71.66
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (acceptedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (submittedVersion)
- Site cant be reached (submittedVersion)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- Wellcome Trust (108437, 105045/Z/14/Z)
- Cancer Research UK (22231, C14303 / A17197)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- batch effects, single-cell RNA sequencing, batch effect correction, mutual nearest neighbors, data integration
- MeSH terms
- Data Analysis, Algorithms, Cluster Analysis, Sequence Analysis, RNA, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
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