Xu, JunJunXuFalconer, CaitlinCaitlinFalconerNguyen, QuanQuanNguyenCrawford, JoannaJoannaCrawfordMc Kinnon, BrettBrettMc Kinnon0000-0002-9881-1252Mortlock, SallySallyMortlockSenabouth, AnneAnneSenabouthAndersen, StaceyStaceyAndersenChiu, Han ShengHan ShengChiuJiang, LongdaLongdaJiangPalpant, Nathan JNathan JPalpantYang, JianJianYangMueller, MichaelMichaelMuellerHewitt, Alex WAlex WHewittPébay, AliceAlicePébayMontgomery, Grant WGrant WMontgomeryPowell, Joseph EJoseph EPowellCoin, Lachlan J MLachlan J MCoin2024-10-282024-10-282019-12-19https://boris-portal.unibe.ch/handle/20.500.12422/185433A 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.enAllele fraction Demultiplexing Doublets Expectation-maximization Genotype-free Hidden Markov Model Machine learning Unsupervised scRNA-seq scSplit600 - Technology::610 - Medicine & healtharticle10.7892/boris.1380453185688310.1186/s13059-019-1852-7