A statistical method for estimating the proportion of differentially expressed genes.
Microarrays have been widely used to identify differentially expressed genes. One related problem is to estimate the proportion of differentially expressed genes. For some complex diseases, the amount of differentially expressed genes may be relatively small and these genes may only have subtly differential expressions. For these microarray data, it is generally difficult to efficiently estimate the proportion of differentially expressed genes. In this study, I propose a likelihood-based method coupled with an expectation-maximization (E-M) algorithm for estimating the proportion of differentially expressed genes. The proposed method has favorable performances if either (i) the P values of differentially expressed genes are homogeneously distributed or (ii) the proportion of differentially expressed genes is relatively small. In both of these situations, I showed through simulations that the proposed method gave satisfactory performances when it was compared to other existing methods. As applications, these methods were applied to two microarray gene expression data sets generated from different platforms.
Lai
Yinglei
Y
Department of Statistics and Biostatistics Center, The George Washington University, 2140 Pennsylvania Avenue, N.W., Washington, DC 20052, USA. ylai@gwu.edu
D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D016013:Likelihood Functions; D008722:Methods; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis