Bioinformatics Advance Access originally published online on May 26, 2006
Bioinformatics 2006 22(15):1863-1870; doi:10.1093/bioinformatics/btl270
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© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Scanning microarrays at multiple intensities enhances discovery of differentially expressed genes
1 Molecular, Cellular and Developmental Biology Program, Iowa State University Ames, IA 50011 USA
2 Department of Genetics, Development and Cell Biology, Iowa State University Ames, IA 50011 USA
3 Interdepartmental Genetics Program, Iowa State University Ames, IA 50011 USA
4 Laurence H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University Ames, IA 50011 USA
5 Center for Plant Genomics, Iowa State University Ames, IA 50011 USA
6 Bioinformatics and Computational Biology Graduate Program, Iowa State University Ames, IA 50011 USA
7 Department of Statistics, Iowa State University Ames, IA 50011 USA
8 Department of Agronomy, Iowa State University Ames, IA 50011 USA
*To whom correspondence should be addressed: Patrick S. Schnable, 2035B Roy J. Carver Co-Lab, Iowa State University, Ames, IA 50011-3650, USA. Tel: +1 515 294 0975; Fax: +1 515 294 5256; Email: schnable{at}iastate.edu
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 <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. Quantitative RT–PCR (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 no. GSE3017 [NCBI GEO] .
Contact: schnable{at}iastate.edu
Supplementary information: Data from these experiments can be viewed at http://www.plantgenomics.iastate.edu/microarray/data/
Received on November 19, 2005; revised on May 19, 2006; accepted on May 20, 2006
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