Evaluation of Statistical Methods for Identification of Differentially Expreseed Genes in Microarray Experiments
Date Issued
2006
Date
2006
Author(s)
Dai, Jia-Yan
DOI
en-US
Abstract
Current statistical approaches to identifying differentially expressed genes are based on tradition hypotheses of equality. However, traditional hypothesis of equality fail to take into consideration the magnitudes of the biologically meaningful fold changes that truly differentiate the expression levels of genes between groups. Due to the large number of genes tested and small number of specimens available for microarray experiments, the false positive rate for differentially expressed genes is extremely high and requires many different adjustments such as Bonferroni’s method, false discovery rate, or use of an arbitrary cutoff for the p-values. All these adjustments do not have any biological justification. Hence, we propose to use the interval hypotheses by consideration of the minimal biologically meaningful expression levels for identification of differentially expressed genes. Based on the interval hypothesis, statistical procedures were proposed and the methods for sample size determination are also given. Statistical properties of the proposed procedures are investigated. A large simulation study was conducted to empirically compare the overall type I error, average type I error and power of the traditional hypothesis using unpaired two-sample t-test, the traditional hypothesis using the unpaired two-sample t-test with Bonferroni adjustment, the fixed fold-change rule, the method of combination of the traditional hypothesis using unpaired two-sample t-test and fixed fold-change rule, and the proposed interval under various combinations of fold changes, variability and sample sizes. Simulation results show that the proposed procedures based on the interval hypothesis not only can control the average type I error rate at the nominal level but also provide sufficient power to detect differentially expressed gene. Numeric data from public domains illustrate the proposed methods.
Subjects
區間假設檢定
整體型一錯誤
平均型一錯誤
檢定力
倍數變化
Interval hypothesis
Type I error
Power
Fold change
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-95-R93621209-1.pdf
Size
23.31 KB
Format
Adobe PDF
Checksum
(MD5):7a1ad760ed9c2bc5670023ab3548751d