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

Xu, Jun; Falconer, Caitlin; Nguyen, Quan; Crawford, Joanna; McKinnon, Brett D.; Mortlock, Sally; Senabouth, Anne; Andersen, Stacey; Chiu, Han Sheng; Jiang, Longda; Palpant, Nathan J; Yang, Jian; Mueller, Michael D.; Hewitt, Alex W; Pébay, Alice; Montgomery, Grant W; Powell, Joseph E; Coin, Lachlan J M (2019). Genotype-free demultiplexing of pooled single-cell RNA-seq. Genome biology, 20(1), p. 290. BioMed Central Ltd. 10.1186/s13059-019-1852-7

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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 Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Gynaecology

UniBE Contributor:

Mc Kinnon, Brett and Mueller, Michael

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1465-6906

Publisher:

BioMed Central Ltd.

Language:

English

Submitter:

Monika Zehr

Date Deposited:

21 Jan 2020 14:43

Last Modified:

26 Jan 2020 02:49

Publisher DOI:

10.1186/s13059-019-1852-7

PubMed ID:

31856883

Uncontrolled Keywords:

Allele fraction Demultiplexing Doublets Expectation-maximization Genotype-free Hidden Markov Model Machine learning Unsupervised scRNA-seq scSplit

BORIS DOI:

10.7892/boris.138045

URI:

https://boris.unibe.ch/id/eprint/138045

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