Genotype-free demultiplexing of pooled single-cell RNA-seq
AuthorXu, J; Falconer, C; Nguyen, Q; Crawford, J; McKinnon, BD; Mortlock, S; Senabouth, A; Andersen, S; Chiu, HS; Jiang, L; ...
Source TitleGenome Biology
University of Melbourne Author/sPebay, Alice
Document TypeJournal Article
CitationsXu, J., Falconer, C., Nguyen, Q., Crawford, J., McKinnon, B. D., Mortlock, S., Senabouth, A., Andersen, S., Chiu, H. S., Jiang, L., Palpant, N. J., Yang, J., Mueller, M. D., Hewitt, A. W., Pebay, A., Montgomery, G. W., Powell, J. E. & Coin, L. J. M. (2019). Genotype-free demultiplexing of pooled single-cell RNA-seq. GENOME BIOLOGY, 20 (1), https://doi.org/10.1186/s13059-019-1852-7.
Access StatusOpen Access
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.
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