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

Tools

Item Type:	Article
Title:	Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Creators Name:	Haghverdi, L., Lun, A.T.L., Morgan, M.D. and Marioni, J.C.
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.
Keywords:	Algorithms, Cluster Analysis, Data Analysis, High-Throughput Nucleotide Sequencing, RNA Sequence Analysis, Single-Cell Analysis
Source:	Nature Biotechnology
ISSN:	1087-0156
Publisher:	Nature Publishing Group
Volume:	36
Number:	5
Page Range:	421-427
Date:	May 2018
Additional Information:	Copyright © 2018 Nature America, Inc. , part of Springer Nature. All rights reserved.
Official Publication:	https://doi.org/10.1038/nbt.4091
External Fulltext:	View full text on PubMed Central
PubMed:	View item in PubMed

Repository Staff Only: item control page