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

Author

Listed:
  • 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
    Tsinghua University)

  • 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
    UPMC Hillman Cancer Center)

  • Robert Lafyatis

    (University of Pittsburgh)

  • Dario A. A. Vignali

    (University of Pittsburgh
    UPMC Hillman Cancer Center
    UPMC Hillman Cancer Center)

  • Kong Chen

    (University of Pittsburgh)

  • Ying Ding

    (University of Pittsburgh)

  • Ming Hu

    (Lerner Research Institute, Cleveland Clinic Foundation)

  • Wei Chen

    (University of Pittsburgh
    Children’s Hospital of Pittsburgh of UPMC, University of Pittsburgh)

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.

Suggested Citation

  • 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 , 2019. "A Bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09639-3
    DOI: 10.1038/s41467-019-09639-3
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    Cited by:

    1. Meiling Zheng & Zhi Hu & Xiaole Mei & Lianlian Ouyang & Yang Song & Wenhui Zhou & Yi Kong & Ruifang Wu & Shijia Rao & Hai Long & Wei Shi & Hui Jing & Shuang Lu & Haijing Wu & Sujie Jia & Qianjin Lu & , 2022. "Single-cell sequencing shows cellular heterogeneity of cutaneous lesions in lupus erythematosus," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    2. Ajita Shree & Musale Krushna Pavan & Hamim Zafar, 2023. "scDREAMER for atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    3. Lin Lin & Wei Shi & Jianbo Ye & Jia Li, 2023. "Multisource single‐cell data integration by MAW barycenter for Gaussian mixture models," Biometrics, The International Biometric Society, vol. 79(2), pages 866-877, June.

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