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Analysis Issues for Gene Expression Array Data

^aDivision of Biostatistics and Epidemiology, Department of Health Evaluation Sciences, University of Virginia School of Medicine, PO Box 800717, Charlottesville, VA 22908. Fax 804-924-8437; e-mail jaeklee@virginia.edu.

	Introduction

 
Gene expression array technologies are rapidly emerging for use in various genome-wide studies in biology and medicine (1)(2)(3). Results from the recently completed sequencing of the human genome predict that the number of genes in the human genome is much smaller (33 000 genes) than anticipated (4). This implies that the major complexity in biological mechanisms in humans lies in the synergistic effects among various genes. Therefore, to decipher the secrets of our life and, ultimately, to find the cures for many human diseases, biologists must deal with multiple genes and their interactive transcripts simultaneously. High-throughput biotechnologies, including gene chip approaches, will play an important role in these studies (5).

However, quality control over thousands of gene expression values and full utilization of the information from these high-throughput data are extremely difficult, and important issues in quality control and bioinformatic approaches have not been resolved (6)(7). A series of careful analyses on the variability of array instrumentation and on the statistical evaluation of gene expression intensities are required for reliable and consistent inference on gene chip data. Specifically, sources of error and their confidence levels on these high-throughput measurements need to be better understood because some can significantly alter our inferences and conclusions (8). The purpose of this report is to dispel three common misconceptions about array experiments.

	Myth 1: A Replicated Gene Chip Experiment Is Needed Only for Confirming Reproducibility

 
It often is believed that reproducibility of gene expression data is high enough to perform bioinformatic discovery without a replicated chip experiment and that duplicated array data are necessary only for confirmation of such a discovery, which may help publication in high-standard journals. Investigators who have successfully obtained resources for high-cost gene chip experiments must optimize their experimental strategy for their study goals within limited . . . [Full Text of this Article]

	Myth 2: Chip Experiments Can Be Done without a Statistical Design

 

	Myth 3: Experimental Confirmation Is the Only Way to Validate Findings

 

	References

 

The following articles in journals at HighWire Press have cited this article:

				S. Bhattacharya and T. J. Mariani Transformation of expression intensities across generations of Affymetrix microarrays using sequence matching and regression modeling Nucleic Acids Res., October 13, 2005; 33(18): e157 - e157. [Abstract] [Full Text] [PDF]

				A. K. Agarwal, P. D. Rogers, S. R. Baerson, M. R. Jacob, K. S. Barker, J. D. Cleary, L. A. Walker, D. G. Nagle, and A. M. Clark Genome-wide Expression Profiling of the Response to Polyene, Pyrimidine, Azole, and Echinocandin Antifungal Agents in Saccharomyces cerevisiae J. Biol. Chem., September 12, 2003; 278(37): 34998 - 35015. [Abstract] [Full Text] [PDF]

				A. R. Lankford, A. M. Byford, K. J. Ashton, B. A. French, J. K. Lee, J. P. Headrick, and G. P. Matherne Gene expression profile of mouse myocardium with transgenic overexpression of A1 adenosine receptors Physiol Genomics, October 29, 2002; 11(2): 81 - 89. [Abstract] [Full Text] [PDF]