FDR-controlling testing procedures and sample size determination for microarrays

被引:37
作者
Li, SYS
Bigler, J
Lampe, JW
Potter, JD
Feng, ZD
机构
[1] Fred Hutchinson Canc Res Ctr, Canc Prevent Res Program, Seattle, WA 98109 USA
[2] Fred Hutchinson Canc Res Ctr, Biostat Program, Seattle, WA 98109 USA
关键词
microarray; FDR-controlling; sample size;
D O I
10.1002/sim.2119
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Microarrays are used increasingly to identify genes that are truly differentially expressed in tissues under different conditions. Planning such studies requires establishing a sample size that will ensure adequate statistical power. For microarray analyses, false discovery rate (FDR) is considered to be an appropriate error measure. Several FDR-controlling procedures have been developed. How these procedures perform for such analyses has not been evaluated thoroughly under realistic assumptions. In order to develop a method of determining sample sizes for these procedures, it needs to be established whether these procedures really control the FDR below the pre-specified level so that the determined sample size indeed provides adequate power. To answer this question, we first conducted simulation studies. Our simulation results showed that these procedures do control the FDR at most situations but under-control the FDR when the proportion of positive genes is small, the most likely scenarios. Thus, these existing procedures can overestimate the power and underestimate the sample size. Accordingly, we developed a simulation-based method to provide more accurate estimates for power and sample size. Copyright (c) 2005 John Wiley & Sons, Ltd.
引用
收藏
页码:2267 / 2280
页数:14
相关论文
共 25 条
  • [1] [Anonymous], 1993, Resampling-based multiple testing: Examples and methods for P-value adjustment
  • [2] Benjamini Y, 2001, ANN STAT, V29, P1165
  • [3] CONTROLLING THE FALSE DISCOVERY RATE - A PRACTICAL AND POWERFUL APPROACH TO MULTIPLE TESTING
    BENJAMINI, Y
    HOCHBERG, Y
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 1995, 57 (01) : 289 - 300
  • [4] Microarrays and genetic epidemiology: A multipurpose tool for a multifaceted field
    Dalma-Weiszhausz, DD
    Chicurel, ME
    Gingeras, TR
    [J]. GENETIC EPIDEMIOLOGY, 2002, 23 (01) : 4 - 20
  • [5] Dudoit S, 2002, STAT SINICA, V12, P111
  • [6] Expression profiling using cDNA microarrays
    Duggan, DJ
    Bittner, M
    Chen, YD
    Meltzer, P
    Trent, JM
    [J]. NATURE GENETICS, 1999, 21 (Suppl 1) : 10 - 14
  • [7] Empirical Bayes methods and false discovery rates for microarrays
    Efron, B
    Tibshirani, R
    [J]. GENETIC EPIDEMIOLOGY, 2002, 23 (01) : 70 - 86
  • [8] Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring
    Golub, TR
    Slonim, DK
    Tamayo, P
    Huard, C
    Gaasenbeek, M
    Mesirov, JP
    Coller, H
    Loh, ML
    Downing, JR
    Caligiuri, MA
    Bloomfield, CD
    Lander, ES
    [J]. SCIENCE, 1999, 286 (5439) : 531 - 537
  • [9] Role of gene expression microarray analysis in finding complex disease genes
    Gu, CC
    Rao, DC
    Stormo, G
    Hicks, C
    Province, MA
    [J]. GENETIC EPIDEMIOLOGY, 2002, 23 (01) : 37 - 56
  • [10] JONES B, 1989, DESIGN ANAL CROSSOVE