GMM-Demux: sample demultiplexing, multiplet detection, experiment planning and novel cell type verification in single cell sequencing.
Identifying and removing multiplets is 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 generated 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, highly accurate and recognized 9 multiplet-induced fake cell types in a PBMC dataset. Overall design: Peripheral blood mononuclear cells were profiled by CITE-seq using a panel of 10 antibodies and cell hashing using 4 HTOs. CD4+ Memory T cells enriched from peripheral blood were profiled by scRNA-seq and cell hashing using 5 HTOs. Each HTO  is constructed with a combination of CD298 and B2M antibodies, labeled with different barcodes.