A mixture factor model with applications to microarray data

• Chaofeng Yuan ^[1] ; Wensheng Zhu ^[1] ; Xuming He ^[2] ; Jianhua Guo ^[1]
  1. [1] Northeast Normal University

     Northeast Normal University

     China

     
  2. [2] University of Michigan–Ann Arbor

     University of Michigan–Ann Arbor

     City of Ann Arbor, Estados Unidos

     
• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 28, Nº. 1, 2019, págs. 60-76
• Idioma: inglés
• DOI: 10.1007/s11749-018-0585-3
• Texto completo no disponible (Saber más ...)
• Resumen

  • Investigators routinely use unidimensional summaries for multidimensional data. In microarray data analysis, for example, the gene expression level is indeed a unidimensional summary of probe-level or SNP measurements. In this paper, we propose a mixture factor model for the low-level data, which enables us to examine the adequacy of a unidimensional summary while accommodating known or latent subgroups in the population. We also develop screening procedures based on the proposed model to identify potentially informative genes in biomedical studies. As shown in our empirical studies, the proposed methods are often more effective than existing methods because the new model goes beyond the conventional unidimensional summaries of gene expressions.