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Genotype-free demultiplexing of pooled single-cell RNA-seq

Citation

Xu, J and Falconer, C and Nguyen, Q and Crawford, J and McKinnon, BD and Mortlock, S and Senabouth, A and Andersen, S and Chiu, HS and Jiang, L and Palpant, NJ and Yang, J and Mueller, MD and Hewitt, AW and Pebay, A and Montgomery, GW and Powell, JE and Coin, LJM, Genotype-free demultiplexing of pooled single-cell RNA-seq, Genome Biology, 20, (1) Article 290. ISSN 1474-760X (2019) [Refereed Article]


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Copyright Statement

© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

DOI: doi:10.1186/s13059-019-1852-7

Abstract

A variety of methods have been developed to demultiplex pooled samples in a single cell RNA sequencing (scRNA-seq) experiment which either require hashtag barcodes or sample genotypes prior to pooling. We introduce scSplit which utilizes genetic differences inferred from scRNA-seq data alone to demultiplex pooled samples. scSplit also enables mapping clusters to original samples. Using simulated, merged, and pooled multi-individual datasets, we show that scSplit prediction is highly concordant with demuxlet predictions and is highly consistent with the known truth in cell-hashing dataset. scSplit is ideally suited to samples without external genotype information and is available at: https://github.com/jon-xu/scSplit.

Item Details

Item Type:Refereed Article
Keywords:allele fraction, demultiplexing, doublets, expectation-maximization, genotype-free, Hidden Markov Model, machine learning, unsupervised, scRNA-seq, scSplit
Research Division:Biological Sciences
Research Group:Genetics
Research Field:Genomics
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the biological sciences
UTAS Author:Hewitt, AW (Professor Alex Hewitt)
ID Code:138041
Year Published:2019
Web of Science® Times Cited:7
Deposited By:Menzies Institute for Medical Research
Deposited On:2020-03-21
Last Modified:2020-04-03
Downloads:5 View Download Statistics

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