Detecting Differentially Expressed Genes in Heterogeneous Disease Using Half Student t test
Date Issued
2009
Date
2009
Author(s)
Hsu, Chun-Lun
Abstract
Gene-expression has been a popular research topic in recent years. Student t-test is commonly adopted to screen disease-related genes. However, when the researches are focused on heterogeneous disease, the means of gene-expression levels between case group and control group may be similar, and thus, the difference would be difficult to be detected by conventional Student t-test. This study proposed half Student t-test to examine heterogeneous disease. Test statistics of half Student t-test only considers sample standard deviation of control group, without considering the sample standard deviation of case group. This study applied Monte Carlo simulation and real gene-expression data of colon cancer to compare the power performance of half Student t-test and conventional Student t-test. Under the simulated scenario, this study found that half Student t-test could have 35% higher statistical power than conventional Student t-test. In addition, after false discovery rate (cut-off point set at 0.05) control of colon cancer gene-expression data, half Student t-test could detect 279 more significant genes than conventional Student t-test. Half Student t-test is easy to execute with good statistical power, and is worth to be recommended as a method of detecting heterogeneous disease gene-expression difference.
Subjects
Student t-test
gene-expression
heterogeneous disease
power
SDGs
Type
thesis
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