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Nat Biotechnol. 2018 Jun;36(5):421-427. doi: 10.1038/nbt.4091. Epub 2018 Apr 2.

Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors.

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European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.
Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany.
Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
Wellcome Trust Sanger Institute, Cambridge, UK.


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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