Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

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, Wei Chen
doi: https://doi.org/10.1101/392662
Zhe Sun
1Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Li Chen
2Department of Health Outcomes Research and Policy, Harrison School of Pharmacy, Auburn University, Auburn, Alabama, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hongyi Xin
3Division of Pulmonary Medicine, Allergy and Immunology; Department of Pediatrics, Children’s Hospital of Pittsburgh of UPMC, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Qianhui Huang
4Department of Biological Sciences, The Dietrich School of Arts & Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Anthony R Cillo
5Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
8Tumor Microenvironment Center, UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tracy Tabib
6Division of Rheumatology and Clinical Rheumatology, Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ying Ding
1Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jay K Kolls
7School of Medicine, Tulane University, New Orleans, Louisiana, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tullia C Bruno
5Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
8Tumor Microenvironment Center, UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Robert Lafyatis
6Division of Rheumatology and Clinical Rheumatology, Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Dario AA Vignali
5Department of Immunology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
8Tumor Microenvironment Center, UPMC Hillman Cancer Center, Pittsburgh, Pennsylvania
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kong Chen
9edicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ming Hu
10Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, Ohio, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Wei Chen
1Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
3Division of Pulmonary Medicine, Allergy and Immunology; Department of Pediatrics, Children’s Hospital of Pittsburgh of UPMC, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

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 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.

Footnotes

  • ↵Contact: wei.chen{at}chp.edu or hum{at}ccf.org.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted August 16, 2018.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
BAMM-SC: A Bayesian mixture model for clustering droplet-based single cell transcriptomic data from population studies
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
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, Wei Chen
bioRxiv 392662; doi: https://doi.org/10.1101/392662
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
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, Wei Chen
bioRxiv 392662; doi: https://doi.org/10.1101/392662

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3697)
  • Biochemistry (7801)
  • Bioengineering (5686)
  • Bioinformatics (21316)
  • Biophysics (10592)
  • Cancer Biology (8193)
  • Cell Biology (11954)
  • Clinical Trials (138)
  • Developmental Biology (6772)
  • Ecology (10411)
  • Epidemiology (2065)
  • Evolutionary Biology (13890)
  • Genetics (9719)
  • Genomics (13083)
  • Immunology (8158)
  • Microbiology (20037)
  • Molecular Biology (7865)
  • Neuroscience (43116)
  • Paleontology (321)
  • Pathology (1279)
  • Pharmacology and Toxicology (2264)
  • Physiology (3358)
  • Plant Biology (7242)
  • Scientific Communication and Education (1314)
  • Synthetic Biology (2009)
  • Systems Biology (5545)
  • Zoology (1130)