Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors.
IF: 68.164
Cited by: 1,341


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.


Gene Expression

MeSH terms

Cluster Analysis
Data Analysis
High-Throughput Nucleotide Sequencing
Sequence Analysis, RNA
Single-Cell Analysis


Haghverdi, Laleh
Lun, Aaron T L
Morgan, Michael D
Marioni, John C

Recommend literature

Similar data