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

Publication Type:

Journal Article


Functional & integrative genomics, Volume 8, Issue 3, p.181-6 (2008)


Algorithms, Computer Simulation, gene expression, Genome, Human, Humans, Leukemia, Myeloid, Likelihood Functions, Models, Genetic, Models, Statistical, Oligonucleotide Array Sequence Analysis, Precursor Cell Lymphoblastic Leukemia-Lymphoma


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.