Journal
GENOME BIOLOGY
Volume 21, Issue 1, Pages -Publisher
BMC
DOI: 10.1186/s13059-020-02084-2
Keywords
Single cell RNA; Multiplets; Rare cell type; Phony cell type; Demultiplex; Sample barcoding
Funding
- National Institute of Health [R01HL137709, U01DK062420, P01AI106684, UL1TR001857]
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Identifying and removing multiplets are essential to improving the scalability and the reliability of single cell RNA sequencing (scRNA-seq). Multiplets create artificial cell types in the dataset. We propose a Gaussian mixture model-based multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes multiplets through sample barcoding, including cell hashing and MULTI-seq. GMM-Demux uses a droplet formation model to authenticate putative cell types discovered from a scRNA-seq dataset. We generate two in-house cell-hashing datasets and compared GMM-Demux against three state-of-the-art sample barcoding classifiers. We show that GMM-Demux is stable and highly accurate and recognizes 9 multiplet-induced fake cell types in a PBMC dataset.
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