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September 2019 A hierarchical Bayesian model for single-cell clustering using RNA-sequencing data
Yiyi Liu, Joshua L. Warren, Hongyu Zhao
Ann. Appl. Stat. 13(3): 1733-1752 (September 2019). DOI: 10.1214/19-AOAS1250


Understanding the heterogeneity of cells is an important biological question. The development of single-cell RNA-sequencing (scRNA-seq) technology provides high resolution data for such inquiry. A key challenge in scRNA-seq analysis is the high variability of measured RNA expression levels and frequent dropouts (missing values) due to limited input RNA compared to bulk RNA-seq measurement. Existing clustering methods do not perform well for these noisy and zero-inflated scRNA-seq data. In this manuscript we propose a Bayesian hierarchical model, called BasClu, to appropriately characterize important features of scRNA-seq data in order to more accurately cluster cells. We demonstrate the effectiveness of our method with extensive simulation studies and applications to three real scRNA-seq datasets.


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Yiyi Liu. Joshua L. Warren. Hongyu Zhao. "A hierarchical Bayesian model for single-cell clustering using RNA-sequencing data." Ann. Appl. Stat. 13 (3) 1733 - 1752, September 2019.


Received: 1 March 2018; Revised: 1 September 2018; Published: September 2019
First available in Project Euclid: 17 October 2019

zbMATH: 07145974
MathSciNet: MR4019156
Digital Object Identifier: 10.1214/19-AOAS1250

Keywords: Bayesian hierarchical model , clustering , Dirichlet process , Gaussian mixture model , missing data , single-cell RNA-sequencing

Rights: Copyright © 2019 Institute of Mathematical Statistics


Vol.13 • No. 3 • September 2019
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