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

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.

SUBMITTER: Haghverdi L

PROVIDER: S-EPMC6152897 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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

Haghverdi Laleh L Lun Aaron T L ATL Morgan Michael D MD Marioni John C JC

Nature biotechnology 20180402 5

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 ...[more]

PMID: 29608177

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