Guan, Shiqin Helen, Bonnett, Laura and Brettschneider, Julia (2009) Using gene subsets in the assessment of microarray data quality for time course experiments. Working Paper. Coventry: University of Warwick. Centre for Research in Statistical Methodology. (Working papers, Vol.2009).

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Abstract

The application of established microarray data quality measures to time course experiments potentially gives misleading results. In particular, genuine biological variation my be mistaken for technical artifacts. We suggest tailoring standard methods to time course data by restricting the assessment to subsets of genes selected on the basis of the experiment. The method is tested on two different kinds of experimental data sets, one from a developmental process and one from a circadian process. With these modifications, quality assessment for microarray data can be tuned to appropriately address the special situation of time course experiments.

Item Type:	Working or Discussion Paper (Working Paper)
Subjects:	Q Science > QA Mathematics
Divisions:	Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	DNA microarrays, Time-series analysis, Genes -- Mathematical models
Series Name:	Working papers
Publisher:	University of Warwick. Centre for Research in Statistical Methodology
Place of Publication:	Coventry
Date:	2009
Volume:	Vol.2009
Number:	No.24
Number of Pages:	18
Status:	Not Peer Reviewed
Access rights to Published version:	Open Access
References:	Affymetrix (2001), Guidelines for assessing data quality, Affymetrix Inc, Santa Clara, CA. GCOS (2004), GeneChip Expression Analysis – Data Analysis Fundamentals, Affymetrix, Inc, Santa Clara, CA. Bolstad, B., Collin, F., Brettschneider, J., Simpson, K., Cope, L., Irizarry, R., and Speed, T. (2005), Quality Assessment of Affymetrix GeneChip Data, in Bioinformatics and Computational Biology Solutions Using R and Bioconductor, eds. Gentleman, R., Carey, V., Huber, W., Irizarry, R., and Dudoit, S., New York: Springer, Statistics for Biology and Health, pp. 33–48. Brettschneider J., Collin F., Bolstad B., and Speed T. (2008), Quality assessment for short oligonucleotide arrays (with 5 commentaries and rejoinder), in Technometrics, 50(3): 241-264 (article), 279-283 (rejoinder). Guan S., Zheng J., and Brettschneider J. (2007) Microarray data quality assessment for developmental time series, in LASR Proceedings, eds. Barber S., Baxter P., and Mardia K., Systems Biology Statistical Bioinformatics, Leeds University Press. Edwards, K., Anderson P., Hall A., Salathia N., Locke J., Lynne J., Straume M., Smith J., Millar A. (2006), Flowering locus C mediates natural variation in the high temperature response of the Arabidopsis circadian clock, Plant Cell, 18: 639-650. Hwang, K., Kong, S., Greenberg, S., and Park, P. (2004), Combining gene expression data from different generations of oligonucleotide arrays, BMC Bioinformatics, 5, e159. Irizarry, R., Bolstad, B., Collin, F., Cope, L., Hobbs, B., and Speed, T. (2003), Summaries of Affymetrix GeneChip probe level data, Nucleic Acids Res, 31, e15. Shewhart,W. (1939), Statistical Method from the Viewpoint of Quality Control, Lanceser, Pennsylvania: Lancester Press, Inc. Tai, Y. and Speed, T. (2006), A multivariate empirical Bayes statstic for replicated microarray time course data, Ann Statist, 34, 2387–2412. Wilson, C. and Miller, C. (2005), Simpleaffy: a BioConductor package for Affymetrix quality control and data analysis, Bioinformatics, 21, 3683–3685.
URI:	http://wrap.warwick.ac.uk/id/eprint/35212

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