Bioinformatics Advance Access originally published online on January 17, 2006
Bioinformatics 2006 22(7):808-814; doi:10.1093/bioinformatics/btk052
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
An interactive power analysis tool for microarray hypothesis testing and generation
Research Center for Genetic Medicine, Children's National Medical Center Washington DC 20010, USA
*To whom correspondence should be addressed.
Motivation: Human clinical projects typically require a priori statistical power analyses. Towards this end, we sought to build a flexible and interactive power analysis tool for microarray studies integrated into our public domain HCE 3.5 software package. We then sought to determine if probe set algorithms or organism type strongly influenced power analysis results.
Results: The HCE 3.5 power analysis tool was designed to import any pre-existing Affymetrix microarray project, and interactively test the effects of user-defined definitions of 
(significance), ß (1 – power), sample size and effect size. The tool generates a filter for all probe sets or more focused ontology-based subsets, with or without noise filters that can be used to limit analyses of a future project to appropriately powered probe sets. We studied projects from three organisms (Arabidopsis, rat, human), and three probe set algorithms (MAS5.0, RMA, dChip PM/MM). We found large differences in power results based on probe set algorithm selection and noise filters. RMA provided high sensitivity for low numbers of arrays, but this came at a cost of high false positive results (24% false positive in the human project studied). Our data suggest that a priori power calculations are important for both experimental design in hypothesis testing and hypothesis generation, as well as for the selection of optimized data analysis parameters.
Availability: The Hierarchical Clustering Explorer 3.5 with the interactive power analysis functions is available at www.cs.umd.edu/hcil/hce or www.cnmcresearch.org/bioinformatics.
Contact: jseo{at}cnmcresearch.org
Received on November 11, 2005; revised on December 23, 2005; accepted on January 9, 2006
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  F. Cordero, M. Botta, and R. A. Calogero Microarray data analysis and mining approaches Brief Funct Genomic Proteomic, January 22, 2008; (2008) elm034v1. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 T. S. Mehta, S. O. Zakharkin, G. L. Gadbury, and D. B. Allison Epistemological issues in omics and high-dimensional biology: give the people what they want Physiol Genomics, December 13, 2006; 28(1): 24 - 32. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. Lechno-Yossef, Q. Fan, S. Ehira, N. Sato, and C. P. Wolk Mutations in Four Regulatory Genes Have Interrelated Effects on Heterocyst Maturation in Anabaena sp. Strain PCC 7120 J. Bacteriol., November 1, 2006; 188(21): 7387 - 7395. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J.-J. Park, J. R. Berggren, M. W. Hulver, J. A Houmard, and E. P. Hoffman GRB14, GPD1, and GDF8 as potential network collaborators in weight loss-induced improvements in insulin action in human skeletal muscle Physiol Genomics, October 11, 2006; 27(2): 114 - 121. [Abstract] [Full Text] [PDF]
|
|