A mixture factor model with applications to microarray data

Chaofeng Yuan (), Wensheng Zhu (), Xuming He () and Jianhua Guo ()
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Chaofeng Yuan: Northeast Normal University
Wensheng Zhu: Northeast Normal University
Xuming He: University of Michigan
Jianhua Guo: Northeast Normal University

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2019, vol. 28, issue 1, No 7, 60-76

Abstract: Abstract 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.

Keywords: Mixture factor models; Mean structure; Unidimensional test; Gene screening; Primary 62H25; Secondary 62P10 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11749-018-0585-3

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