title: Sources of variation in false discovery rate estimation include sample size, correlation, and inherent differences between groups.
abstract: BACKGROUND
  High-throughtput technologies enable the testing of tens of thousands of measurements simultaneously. Identification of genes that are differentially expressed or associated with clinical outcomes invokes the multiple testing problem. False Discovery Rate (FDR) control is a statistical method used to correct for multiple comparisons for independent or weakly dependent test statistics. Although FDR control is frequently applied to microarray data analysis, gene expression is usually correlated, which might lead to inaccurate estimates. In this paper, we evaluate the accuracy of FDR estimation.
  
  
  METHODS
  Using two real data sets, we resampled subgroups of patients and recalculated statistics of interest to illustrate the imprecision of FDR estimation. Next, we generated many simulated data sets with block correlation structures and realistic noise parameters, using the Ultimate Microarray Prediction, Inference, and Reality Engine (UMPIRE) R package. We estimated FDR using a beta-uniform mixture (BUM) model, and examined the variation in FDR estimation.
  
  
  RESULTS
  The three major sources of variation in FDR estimation are the sample size, correlations among genes, and the true proportion of differentially expressed genes (DEGs). The sample size and proportion of DEGs affect both magnitude and precision of FDR estimation, while the correlation structure mainly affects the variation of the estimated parameters.
  
  
  CONCLUSIONS
  We have decomposed various factors that affect FDR estimation, and illustrated the direction and extent of the impact. We found that the proportion of DEGs has a significant impact on FDR; this factor might have been overlooked in previous studies and deserves more thought when controlling FDR.
authors: Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.
authors: Jiexin
authors: J
authors: Zhang
authors: Kevin R
authors: KR
authors: Coombes
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D008224:Lymphoma, Follicular / Q000235:genetics / Q000401:mortality; D008297:Male; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D011471:Prostatic Neoplasms / Q000235:genetics / Q000401:mortality; D012044:Regression Analysis; D018401:Sample Size; D012984:Software