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 RNAsequencing (scRNA-seq). Multiplets create artificial cell types in the dataset. We propose a Gaussian-mixture-modelbased 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, highly accurate and recognizes 9 multiplet-induced fake cell types in a PBMC dataset.