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A mixture factor model with applications to microarray data

Author

Listed:
  • Chaofeng Yuan

    (Northeast Normal University)

  • Wensheng Zhu

    (Northeast Normal University)

  • Xuming He

    (University of Michigan)

  • Jianhua Guo

    (Northeast Normal University)

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.

Suggested Citation

  • Chaofeng Yuan & Wensheng Zhu & Xuming He & Jianhua Guo, 2019. "A mixture factor model with applications to microarray data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 60-76, March.
    • Handle: RePEc:spr:testjl:v:28:y:2019:i:1:d:10.1007_s11749-018-0585-3
    DOI: 10.1007/s11749-018-0585-3
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    References listed on IDEAS

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    6. Hu, Jianhua & Wright, Fred A. & Zou, Fei, 2006. "Estimation of Expression Indexes for Oligonucleotide Arrays Using the Singular Value Decomposition," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 41-50, March.
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