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

Jun XuCaitlin FalconerQuan NguyenJoanna CrawfordBrett D McKinnonSally MortlockAnne SenabouthStacey AndersenHan Sheng ChiuLongda JiangNathan J PalpantJian YangMichael D MuellerAlex W HewittAlice PébayGrant W MontgomeryJoseph E PowellLachlan J M Coin
Published in: Genome biology (2019)
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.
Keyphrases
  • single cell
  • rna seq
  • high throughput
  • phase iii
  • high resolution
  • healthcare
  • stem cells
  • machine learning
  • gene expression
  • genome wide
  • electronic health record
  • big data
  • bone marrow
  • cell therapy