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