流行病學與預防醫學研究所HSU, CHUN-LUNCHUN-LUNHSULEE, WEN-CHUNGWEN-CHUNGLEE2011-06-242018-06-292011-06-242018-06-292010http://ntur.lib.ntu.edu.tw//handle/246246/235810Background 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 alpha 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 similar to 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 similar to 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.en-USStudent's t-testgene expressionheterogeneous diseaseepidemiological methods[SDGs]SDG3journal article