Journal
NATURE BIOTECHNOLOGY
Volume 36, Issue 5, Pages 421-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/nbt.4091
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Funding
- Wellcome Trust [108437/Z/15, 105045/Z/14/Z]
- CRUK [17197]
- EMBL
- Wellcome Trust [105045/Z/14/Z] Funding Source: researchfish
- Wellcome Trust [105045/Z/14/Z] Funding Source: Wellcome Trust
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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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