t-mixture model approach for detecting differentially expressed genes in microarrays

• Published in: Functional & integrative genomics Vol. 8; no. 3; pp. 181 - 186
• Main Authors: Jiao, Shuo, Zhang, Shunpu
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Berlin/Heidelberg : Springer-Verlag 01.08.2008; Springer-Verlag; Springer; Springer Nature B.V
• Subjects: Algorithms; Animal Genetics and Genomics; Biochemistry; Bioinformatics; Biological and medical sciences; Biomedical and Life Sciences; Cell Biology; Computer Simulation; Fundamental and applied biological sciences. Psychology; Gene Expression; gene expression regulation; genes; genetics; Genome, Human; genomics; Humans; leukemia; Leukemia, Myeloid; Leukemia, Myeloid - genetics; Life Sciences; Likelihood Functions; methods; microarray technology; Microbial Genetics and Genomics; Models, Genetic; Models, Statistical; Molecular and cellular biology; Molecular genetics; Normal mixture; Oligonucleotide Array Sequence Analysis; Oligonucleotide Array Sequence Analysis - methods; Original Paper; Permutation; Plant Genetics and Genomics; Precursor Cell Lymphoblastic Leukemia-Lymphoma; Precursor Cell Lymphoblastic Leukemia-Lymphoma - genetics; probability; t mixture; mixture; Permutation; Normal mixture; t mixture; Models; Gene; Detection; Normal mixture .Permutation; Genomics
• ISSN: 1438-793X; 1438-7948
• DOI: 10.1007/s10142-007-0071-6

The finite mixture model approach has attracted much attention in analyzing microarray data due to its robustness to the excessive variability which is common in the microarray data. Pan (2003) proposed to use the normal mixture model method (MMM) to estimate the distribution of a test statistic and its null distribution. However, considering the fact that the test statistic is often of t-type, our studies find that the rejection region from MMM is often significantly larger than the correct rejection region, resulting an inflated type I error. This motivates us to propose the t-mixture model (TMM) approach. In this paper, we demonstrate that TMM provides significantly more accurate control of the probability of making type I errors (hence of the familywise error rate) than MMM. Finally, TMM is applied to the well-known leukemia data of Golub et al. (1999). The results are compared with those obtained from MMM.