Genotype-free demultiplexing of pooled single-cell RNA-seq.
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BORIS DOI
Publisher DOI
PubMed ID
31856883
Description
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.
Date of Publication
2019-12-19
Publication Type
Article
Subject(s)
600 - Technology::610 - Medicine & health
Keyword(s)
Allele fraction Demultiplexing Doublets Expectation-maximization Genotype-free Hidden Markov Model Machine learning Unsupervised scRNA-seq scSplit
Language(s)
en
Contributor(s)
Xu, Jun | |
Falconer, Caitlin | |
Nguyen, Quan | |
Crawford, Joanna | |
Mortlock, Sally | |
Senabouth, Anne | |
Andersen, Stacey | |
Chiu, Han Sheng | |
Jiang, Longda | |
Palpant, Nathan J | |
Yang, Jian | |
Hewitt, Alex W | |
Pébay, Alice | |
Montgomery, Grant W | |
Powell, Joseph E | |
Coin, Lachlan J M |
Additional Credits
Universitätsklinik für Frauenheilkunde
Series
Genome biology
Publisher
BioMed Central Ltd.
ISSN
1465-6906
Access(Rights)
open.access