PT  - JOURNAL ARTICLE
AU  - Zhe Sun
AU  - Li Chen
AU  - Hongyi Xin
AU  - Qianhui Huang
AU  - Anthony R Cillo
AU  - Tracy Tabib
AU  - Ying Ding
AU  - Jay K Kolls
AU  - Tullia C Bruno
AU  - Robert Lafyatis
AU  - Dario AA Vignali
AU  - Kong Chen
AU  - Ming Hu
AU  - Wei Chen
TI  - BAMM-SC: A Bayesian mixture model for clustering droplet-based single cell transcriptomic data from population studies
AID  - 10.1101/392662
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 392662
4099  - http://biorxiv.org/content/early/2018/08/16/392662.short
4100  - http://biorxiv.org/content/early/2018/08/16/392662.full
AB  - The recently developed droplet-based single cell transcriptome sequencing (scRNA-seq) technology makes it feasible to perform a population-scale scRNA-seq study, in which the transcriptome is measured for tens of thousands of single cells from multiple individuals. Despite the advances of many clustering methods, there are few tailored methods for population-scale scRNA-seq studies. Here, we have developed a BAyesiany Mixture Model for Single Cell sequencing (BAMM-SC) method to cluster scRNA-seq data from multiple individuals simultaneously. Specifically, BAMM-SC takes raw data as input and can account for data heterogeneity and batch effect among multiple individuals in a unified Bayesian hierarchical model framework. Results from extensive simulations and application of BAMM-SC to in-house scRNA-seq datasets using blood, lung and skin cells from humans or mice demonstrated that BAMM-SC outperformed existing clustering methods with improved clustering accuracy and reduced impact from batch effects. BAMM-SC has been implemented in a user-friendly R package with a detailed tutorial available on www.pitt.edu/~Cwec47/singlecell.html.