1. Genotype-free demultiplexing of pooled single-cell RNA-seq
- Author
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Grant W. Montgomery, Nathan J. Palpant, Longda Jiang, Michael D. Mueller, Brett McKinnon, Alex W. Hewitt, Joanna Crawford, Jun Xu, Lachlan J. M. Coin, Sally-Anne Mortlock, Quan Nguyen, Anne Senabouth, Han Sheng Chiu, Caitlin Falconer, Stacey B. Andersen, Alice Pébay, Joseph E. Powell, and Jian Yang
- Subjects
lcsh:QH426-470 ,Sequence analysis ,Expectation-maximization ,Pooling ,Method ,RNA-Seq ,Sample (statistics) ,610 Medicine & health ,Computational biology ,Biology ,Hidden Markov Model ,Unsupervised ,03 medical and health sciences ,0302 clinical medicine ,Single-cell analysis ,Genotype ,scRNA-seq ,Machine learning ,Demultiplexing ,Allele fraction ,Humans ,lcsh:QH301-705.5 ,030304 developmental biology ,0303 health sciences ,Genotype-free ,Sequence Analysis, RNA ,Doublets ,lcsh:Genetics ,lcsh:Biology (General) ,scSplit ,Single-Cell Analysis ,030217 neurology & neurosurgery ,Software - 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
- Published
- 2019
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