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

Item Type:Article
Title:Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Creators Name:Haghverdi, L. and Lun, A.T.L. and 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
Official Publication:https://doi.org/10.1038/nbt.4091
PubMed:View item in PubMed
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https://edoc.mdc-berlin.de/19026/Preprint version

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