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.
"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