Estimating the false discovery rate using mixed normal distribution for identifying differentially expressed genes in microarray data analysis.

ABSTRACT: The recent development of DNA microarray technology allows us to measure simultaneously the expression levels of thousands of genes and to identify truly correlated genes with anticancer drug response (differentially expressed genes) from many candidate genes. Significance Analysis of Microarray (SAM) is often used to estimate the false discovery rate (FDR), which is an index for optimizing the identifiability of differentially expressed genes, while the accuracy of the estimated FDR by SAM is not necessarily confirmed. We propose a new method for estimating the FDR assuming a mixed normal distribution on the test statistic and examine the performance of the proposed method and SAM using simulated data. The simulation results indicate that the accuracy of the estimated FDR by the proposed

SUBMITTER: Hirakawa A 

PROVIDER: S-EPMC2675830 | biostudies-literature | 2008 Jan

REPOSITORIES: biostudies-literature