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


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 (, 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


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:

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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