Bioinformatics Advance Access originally published online on June 4, 2004
Bioinformatics 2004 20(16):2821-2828; doi:10.1093/bioinformatics/bth336
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Bioinformatics vol. 20 issue 16 © Oxford University Press 2004; all rights reserved.
A statistical framework for the design of microarray experiments and effective detection of differential gene expression
MRC Toxicology Unit, Hodgkin Building, Lancaster Road, University of Leicester, Leicester, UK
Received on November 18, 2003; revised on April 27, 2004; accepted on May 18, 2004
Advance Access Publication June 4, 2004
Motivation: Microarray experiments generate a high data volume. However, often due to financial or experimental considerations, e.g. lack of sample, there is little or no replication of the experiments or hybridizations. These factors combined with the intrinsic variability associated with the measurement of gene expression can result in an unsatisfactory detection rate of differential gene expression (DGE). Our motivation was to provide an easy to use measure of the success rate of DGE detection that could find routine use in the design of microarray experiments or in post-experiment assessment.
Results: In this study, we address the problem of both random errors and systematic biases in microarray experimentation. We propose a mathematical model for the measured data in microarray experiments and on the basis of this model present a t-based statistical procedure to determine DGE. We have derived a formula to determine the success rate of DGE detection that takes into account the number of microarrays, the number of genes, the magnitude of DGE, and the variance from biological and technical sources. The formula and look-up tables based on the formula, can be used to assist in the design of microarray experiments. We also propose an ad hoc method for estimating the fraction of non-differentially expressed genes within a set of genes being tested. This will help to increase the power of DGE detection.
Availability: The functions to calculate the success rate of DGE detection have been implemented as a Java application, which is accessible at http://www.le.ac.uk/mrctox/microarray_lab/Microarray_Softwares/Microarray_Softwares.htm
Supplementary information at ftp://alcyone.mrc.le.ac.uk/Pub/twg1/BioInf03-0661suppl.pdf
Contact: sdz1{at}le.ac.uk; twg1{at}le.ac.uk
* Correspondence can be addressed to either author.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  S. M Johnson, M. Maleki-Dizaji, J. A Styles, and I. N H White Ishikawa cells exhibit differential gene expression profiles in response to oestradiol or 4-hydroxytamoxifen Endocr. Relat. Cancer, June 1, 2007; 14(2): 337 - 350. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 K. Ridd, S.-D. Zhang, R. E. Edwards, R. Davies, P. Greaves, A. Wolfreys, A. G. Smith, and T. W. Gant Association of gene expression with sequential proliferation, differentiation and tumor formation in murine skin Carcinogenesis, August 1, 2006; 27(8): 1556 - 1566. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S.-D. Zhang and T. W. Gant Effect of pooling samples on the efficiency of comparative studies using microarrays Bioinformatics, December 15, 2005; 21(24): 4378 - 4383. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 R. Davies, A. Schuurman, C. R. Barker, B. Clothier, T. Chernova, F. M. Higginson, D. J. Judah, D. Dinsdale, R. E. Edwards, P. Greaves, et al. Hepatic Gene Expression in Protoporphyic Fech Mice Is Associated with Cholestatic Injury but Not a Marked Depletion of the Heme Regulatory Pool Am. J. Pathol., April 1, 2005; 166(4): 1041 - 1053. [Abstract] [Full Text] [PDF]
|
|