BAMM-SC: A Bayesian mixture model for clustering droplet-based single cell transcriptomic data from population studies

Zhe Sun, Li Chen, Hongyi Xin, Qianhui Huang, Anthony R Cillo, Tracy Tabib, Ying Ding, Jay K Kolls, Tullia C Bruno, Robert Lafyatis, Dario AA Vignali, Kong Chen, Ming Hu, and Wei Chen

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Aug 22, 2018
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Received Date: 6th August 2018

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 BAyesian 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 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 available on

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This is an abstract of a preprint hosted on an independent third party site. It has not been peer reviewed but is currently under consideration at Nature Communications.

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