Authors:
L Haghverdi, ATL Lun, MD Morgan, JC Marioni
Journal name: 
Nat Biotechnol
Citation info: 
36(5):421-427
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.
DOI: 
http://doi.org/10.1038/nbt.4091
Research group: 
Marioni Group
E-pub date: 
01 Jun 2018