Detecting differentially expressed genes in heterogeneous diseases using half Student's t-test.
BACKGROUND
Microarray technology provides information about hundreds and thousands of gene-expression data in a single experiment. To search for disease-related genes, researchers test for those genes that are differentially expressed between the case subjects and the control subjects.


METHODS
The authors propose a new test, the 'half Student's t-test', specifically for detecting differentially expressed genes in heterogeneous diseases. Monte-Carlo simulation shows that the test maintains the nominal α level quite well for both normal and non-normal distributions. Power of the half Student's t is higher than that of the conventional 'pooled' Student's t when there is heterogeneity in the disease under study. The power gain by using the half Student's t can reach ∼10% when the standard deviation of the case group is 50% larger than that of the control group.


RESULTS
Application to a colon cancer data reveals that when the false discovery rate (FDR) is controlled at 0.05, the half Student's t can detect 344 differentially expressed genes, whereas the pooled Student's t can detect only 65 genes. Or alternatively, if only 50 genes are to be selected, the FDR for the pooled Student's t has to be set at 0.0320 (false positive rate of ∼3%), but for the half Student's t, it can be at as low as 0.0001 (false positive rate of about one per ten thousands).


CONCLUSIONS
The half Student's t-test is to be recommended for the detection of differentially expressed genes in heterogeneous diseases.
Research Centre for Genes, Environment and Human Health, College of Public Health, National Taiwan University, Taipei, Taiwan ROC.
Chun-Lun
CL
Hsu
Wen-Chung
WC
Lee
D003110:Colonic Neoplasms / Q000453:epidemiology / Q000235:genetics; D003198:Computer Simulation; D015870:Gene Expression; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D018709:Statistics, Nonparametric