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

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.

SUBMITTER: Xu J

PROVIDER: S-EPMC6921391 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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

Xu Jun J Falconer Caitlin C Nguyen Quan Q Crawford Joanna J McKinnon Brett D BD Mortlock Sally S Senabouth Anne A Andersen Stacey S Chiu Han Sheng HS Jiang Longda L Palpant Nathan J NJ Yang Jian J Mueller Michael D MD Hewitt Alex W AW Pébay Alice A Montgomery Grant W GW Powell Joseph E JE Coin Lachlan J M LJM

Genome biology 20191219 1

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 w ...[more]

PMID: 31856883

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