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February 2020 Model-Based Approach to the Joint Analysis of Single-Cell Data on Chromatin Accessibility and Gene Expression
Zhixiang Lin, Mahdi Zamanighomi, Timothy Daley, Shining Ma, Wing Hung Wong
Statist. Sci. 35(1): 2-13 (February 2020). DOI: 10.1214/19-STS714

Abstract

Unsupervised methods, including clustering methods, are essential to the analysis of single-cell genomic data. Model-based clustering methods are under-explored in the area of single-cell genomics, and have the advantage of quantifying the uncertainty of the clustering result. Here we develop a model-based approach for the integrative analysis of single-cell chromatin accessibility and gene expression data. We show that combining these two types of data, we can achieve a better separation of the underlying cell types. An efficient Markov chain Monte Carlo algorithm is also developed.

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Zhixiang Lin. Mahdi Zamanighomi. Timothy Daley. Shining Ma. Wing Hung Wong. "Model-Based Approach to the Joint Analysis of Single-Cell Data on Chromatin Accessibility and Gene Expression." Statist. Sci. 35 (1) 2 - 13, February 2020. https://doi.org/10.1214/19-STS714

Information

Published: February 2020
First available in Project Euclid: 3 March 2020

MathSciNet: MR4071354
Digital Object Identifier: 10.1214/19-STS714

Rights: Copyright © 2020 Institute of Mathematical Statistics

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Vol.35 • No. 1 • February 2020
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