GMM-Demux: sample demultiplexing, multiplet detection, experiment planning, and novel cell-type verification in single cell sequencing
Hongyi Xin, Qiuyu Lian, Yale Jiang, Jiadi Luo, Xinjun Wang, Carla Erb, Zhongli Xu, Xiaoyi Zhang, Elisa Heidrich-O’Hare, Qi Yan, Richard H. Duerr, Kong Chen, Wei Chen
Abstract
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.