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.
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Acknowledgements
This study is partially supported by the Natural Science Foundation of China Grants 11631003, 11690012, 11771072 and 11371083. The authors thank three referees for their helpful comments that led to an improvement of the paper.
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This article contains supplementary material. In the supplement we provide the detailed proofs for the theorems in Appendix A, the estimation process in Appendix B, and additional results for real data analysis in Appendix C (pdf 331 KB)
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Yuan, C., Zhu, W., He, X. et al. A mixture factor model with applications to microarray data. TEST 28, 60–76 (2019). https://doi.org/10.1007/s11749-018-0585-3
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DOI: https://doi.org/10.1007/s11749-018-0585-3