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. 2019 Dec 19;20(1):290.
doi: 10.1186/s13059-019-1852-7.

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

Jun Xu  1 , Caitlin Falconer  2 , Quan Nguyen  2 , Joanna Crawford  2 , Brett D McKinnon  2   3 , Sally Mortlock  2 , Anne Senabouth  4 , Stacey Andersen  1   2 , Han Sheng Chiu  2 , Longda Jiang  2 , Nathan J Palpant  1   2 , Jian Yang  2   5 , Michael D Mueller  3 , Alex W Hewitt  6   7   8 , Alice Pébay  9   6   7 , Grant W Montgomery  1   2 , Joseph E Powell  10   4 , Lachlan J M Coin  11   12   13   14   15
Affiliations

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

Jun Xu et al. Genome Biol. .

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.

Keywords: Allele fraction; Demultiplexing; Doublets; Expectation-maximization; Genotype-free; Hidden Markov Model; Machine learning; Unsupervised; scRNA-seq; scSplit.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Results on simulated, merged hash-tagged scRNA-seq datasets confirmed scSplit a useful tool to demultiplex pooled single cells. a Confusion matrix showing scSplit demultiplexing results on simulated 2-, 3-, 4- and 8-mix; b TPR and FDR of for singlets and doublets predicted by scSplit and demuxlet compared to known truth before merging; c TPR and FDR of for singlets and doublets predicted by scSplit and demuxlet compared to cell hashing tags
Fig. 2
Fig. 2
Results of scSplit on pooled PBMC scRNA-seq and that on a set of pooled fibroblast samples. a Singlet TPR and FDR compared to demuxlet predictions on pooled PBMC scRNA-seq. b Violin plot of singlet TPR and FDR for five 7- or 8-mixed samples based on scSplit vs demuxlet
Fig. 3
Fig. 3
Batch effect during sequencing runs found in comparison of individual runs was obvious compared to that in pooled scRNA-seq data. a UMAP for three individually sequenced samples. b UMAP for three individually sequenced and normalized samples. c UMAP for pooled sequencing of same three individual samples, samples marked based on demultiplexing results using scSplit. d UMAP for pooled sequencing of same three individual samples, normalized by total sample reads
Fig. 4
Fig. 4
The overall pipeline of scSplit tool. a SNV identified based on reads from all cells which have similar or different genotypes. b Alternative and reference allele count matrices built from each read in the pooled-sequenced BAM at the identified informative SNVs. c Initial allele fraction model constructed from the initial cell seeds and their allele counts. d Expectation-maximization process to find the most optimized allele fraction model, based on which the cells are assigned to clusters. e Presence/Absence matrix of alternative alleles generated from the cell assignments. f Minimum set of distinguishing variants found to be used to map clusters with samples
See this image and copyright information in PMC

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