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

• Zhixiang Lin ^[1] ; Mahdi Zamanighomi ^[3] ; Timothy Daley ^[2] ; Shining Ma ^[2] ; Wong, Wing Hung ^[2]
  1. [1] Chinese University of Hong Kong

     Chinese University of Hong Kong

     RAE de Hong Kong (China)

     
  2. [2] Stanford University

     Stanford University

     Estados Unidos

     
  3. [3] Broad Institute of MIT and Harvard

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• Localización: Statistical science, ISSN 0883-4237, Vol. 35, Nº. 1, 2020 (Ejemplar dedicado a: Statistics and Science), págs. 2-13
• Idioma: inglés
• DOI: 10.1214/19-sts714
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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.