![[feed]](/style/images/feed-icon-14x14.png){kind=icon}
| 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 | ||||
| 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 | ||||
| Related to: |
|
Repository Staff Only: item control page
