Resumen de Model-Based Approach to the Joint Analysis of Single-Cell Data on Chromatin Accessibility and Gene Expression
Zhixiang Lin, Mahdi Zamanighomi, Timothy J. Daley, Shining Ma, Wing H. Wong
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.
