Model-Based Approach to the Joint Analysis of Single-Cell Data on Chromatin Accessibility and Gene Expression
Publication in refereed journal

香港中文大學研究人員

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摘要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.
著者Zhixiang Lin, Mahdi Zamanighomi, Timothy Daley, Shining Ma, Wing Hung Wong
期刊名稱Statistical Science
出版年份2020
月份2
卷號35
期次1
頁次2 - 13
國際標準期刊號0883-4237
語言美式英語

上次更新時間 2020-15-11 於 02:25