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

Zhe Sun, Li Chen, Hongyi Xin, Yale Jiang, Qianhui Huang, Anthony R. Cillo, Tracy Tabib, Jay K. Kolls, Tullia C. Bruno, Robert Lafyatis, Dario A. A. Vignali, Kong Chen, Ying Ding (), Ming Hu () and Wei Chen ()
Additional contact information
Zhe Sun: University of Pittsburgh
Li Chen: Harrison School of Pharmacy, Auburn University
Hongyi Xin: Children’s Hospital of Pittsburgh of UPMC, University of Pittsburgh
Yale Jiang: Children’s Hospital of Pittsburgh of UPMC, University of Pittsburgh
Qianhui Huang: University of Michigan
Anthony R. Cillo: University of Pittsburgh
Tracy Tabib: University of Pittsburgh
Jay K. Kolls: Tulane University
Tullia C. Bruno: University of Pittsburgh
Robert Lafyatis: University of Pittsburgh
Dario A. A. Vignali: University of Pittsburgh
Kong Chen: University of Pittsburgh
Ying Ding: University of Pittsburgh
Ming Hu: Lerner Research Institute, Cleveland Clinic Foundation
Wei Chen: University of Pittsburgh

Nature Communications, 2019, vol. 10, issue 1, 1-10

Abstract: Abstract 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 develop a Bayesian mixture model for single-cell sequencing (BAMM-SC) method to cluster scRNA-seq data from multiple individuals simultaneously. BAMM-SC takes raw count data as input and accounts for data heterogeneity and batch effect among multiple individuals in a unified Bayesian hierarchical model framework. Results from extensive simulation studies and applications of BAMM-SC to in-house experimental scRNA-seq datasets using blood, lung and skin cells from humans or mice demonstrate that BAMM-SC outperformed existing clustering methods with considerable improved clustering accuracy, particularly in the presence of heterogeneity among individuals.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.nature.com/articles/s41467-019-09639-3 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09639-3

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-019-09639-3

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().