Model-Based Approach to the Joint Analysis of Single-Cell Data on Chromatin Accessibility and Gene Expression
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AbstractUnsupervised 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.
All Author(s) ListZhixiang Lin, Mahdi Zamanighomi, Timothy Daley, Shining Ma, Wing Hung Wong
Journal nameStatistical Science
Volume Number35
Issue Number1
Pages2 - 13
LanguagesEnglish-United States

Last updated on 2020-23-10 at 02:35