Ranking analysis of microarray data: a powerful method for identifying differentially expressed genes.
Microarray technology provides a powerful tool for the expression profile of thousands of genes simultaneously, which makes it possible to explore the molecular and metabolic etiology of the development of a complex disease under study. However, classical statistical methods and technologies fail to be applicable to microarray data. Therefore, it is necessary and motivating to develop powerful methods for large-scale statistical analyses. In this paper, we described a novel method, called Ranking Analysis of Microarray Data (RAM). RAM, which is a large-scale two-sample t-test method, is based on comparisons between a set of ranked T statistics and a set of ranked Z values (a set of ranked estimated null scores) yielded by a "randomly splitting" approach instead of a "permutation" approach and a two-simulation strategy for estimating the proportion of genes identified by chance, i.e., the false discovery rate (FDR). The results obtained from the simulated and observed microarray data show that RAM is more efficient in identification of genes differentially expressed and estimation of FDR under undesirable conditions such as a large fudge factor, small sample size, or mixture distribution of noises than Significance Analysis of Microarrays.
Tan
Yuan-De
YD
Institute of Molecular Medicine, School of Public Health, University of Texas at Houston, Houston, TX 77030, USA.
Fornage
Myriam
M
Institute of Molecular Medicine, School of Public Health, University of Texas at Houston, Houston, TX 77030, USA.
Fu
Yun-Xin
YX
Laboratory for Conservation and Utilization of Bioresources, Yunnan University, Kunming, Yunnan 650, China; Human Genetics Center, School of Public Health, University of Texas at Houston, Houston, TX 77030, USA. Electronic address: Yunxin.fu@uth.tmc.edu.
D000818:Animals; D019295:Computational Biology / Q000379:methods*; D005189:False Positive Reactions; D020869:Gene Expression Profiling; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011506:Proteins / Q000235:genetics / Q000378:metabolism*; D011897:Random Allocation; D051381:Rats; D018401:Sample Size; D020521:Stroke
D011506:Proteins