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Bioinformatics Advance Access published online on May 26, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl270
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© 2006 The Author(s)
Received November 19, 2005
Revised May 19, 2006
Accepted May 20, 2006

Article

Scanning microarrays at multiple intensities enhances discovery of differentially expressed genes

David S. Skibbe 1, Xiujuan Wang 2, Xuefeng Zhao 3, Lisa A. Borsuk 4, Dan Nettleton 5, and Patrick S. Schnable 6 *

1 Molecular, Cellular and Developmental Biology Program, Iowa State University, Ames, Iowa 50011 USA; Department of Genetics, Development and Cell Biology, Iowa State University, Ames, Iowa 50011 USA; Current Address: Stanford University, 385 Serra Mall, Stanford, CA 94305-5020
2 Department of Genetics, Development and Cell Biology, Iowa State University, Ames, Iowa 50011 USA; Interdepartmental Genetics Program, Iowa State University, Ames, Iowa 50011 USA
3 Laurence H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, Iowa 50011 USA; Center for Plant Genomics, Iowa State University, Ames, Iowa 50011 USA
4 Bioinformatics and Computational Biology Graduate Program, Iowa State University, Ames, Iowa 50011 USA
5 Department of Statistics, Iowa State University, Ames, Iowa 50011 USA
6 Molecular, Cellular and Developmental Biology Program, Iowa State University, Ames, Iowa 50011 USA; Department of Genetics, Development and Cell Biology, Iowa State University, Ames, Iowa 50011 USA; Interdepartmental Genetics Program, Iowa State University, Ames, Iowa 50011 USA; Center for Plant Genomics, Iowa State University, Ames, Iowa 50011 USA; Department of Agronomy, Iowa State University, Ames, Iowa 50011 USA

* To whom correspondence should be addressed.
Patrick S. Schnable, E-mail: schnable{at}iastate.edu


  Abstract

Motivation: Scanning parameters are often overlooked when optimizing microarray experiments. A scanning approach that extends the dynamic data range by acquiring multiple scans of different intensities has been developed.

Results: Data from each of three scan intensities (low, medium, high) were analyzed separately using multiple scan and linear regression approaches to identify and compare the sets of genes that exhibit statistically significant differential expression. In the multiple scan approach only one-third of the differentially expressed genes were shared among the three intensities, and each scan intensity identified unique sets of differentially expressed genes. The set of differentially expressed genes from any one scan amounted to less than 70% of the total number of genes identified in at least one scan. The average signal intensity of genes that exhibited statistically significant changes in expression was highest for the low-intensity scan and lowest for the high-intensity scan, suggesting that low-intensity scans may be best for detecting expression differences in high-signal genes, while high-intensity scans may be best for detecting expression differences in low-signal genes. Comparison of the differentially expressed genes identified in the multiple scan and linear regression approaches revealed that the multiple scan approach effectively identifies a subset of statistically significant genes that linear regression approach is unable to identify. qRT-PCR tests demonstrated that statistically significant differences identified at all three scan intensities can be verified.

Availability: The data presented can be viewed at http://www.ncbi.nlm.nih.gov/geo/ under GEO Accession GSE3017.

Supplementary information: Data from these experiments can be viewed at http://www.plantgenomics.iastate.edu/microarray/data/.

Conflicts of interest: None declared.

Associate Editor: John Quackenbush
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