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Proceedings Paper

Statistical inference methods for gene expression arrays
Author(s): Robert Nadon; Peide Shi; Adonis Skandalis; Erik Woody; Hermann Hubschle; Edward Susko; Nezar Rghei; Peter Ramm
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Paper Abstract

Gene expression arrays present unique challenges for statistical inference. They typically small number of replicated expression values in array studies make the use of standard parametric statistical tests problematic. Such test have low sensitivity and return potentially inaccurate probability values. This paper describes novel alternative statistical modeling procedures which circumvent these difficulties by pooling random error estimates obtained from replicate expression values. The procedures, which can be used with both micro- and macro-arrays, include outlier detection, confidence intervals, statistical test of differences between conditions, and statistical power analysis for determining number of replicates needed to detect between-condition differences of specified magnitude. The methods are illustrated with experimental data.

Paper Details

Date Published: 4 June 2001
PDF: 10 pages
Proc. SPIE 4266, Microarrays: Optical Technologies and Informatics, (4 June 2001); doi: 10.1117/12.427999
Show Author Affiliations
Robert Nadon, Imaging Research Inc. and Brock Univ. (Canada)
Peide Shi, Imaging Research Inc. (Canada)
Adonis Skandalis, Brock Univ. (Canada)
Erik Woody, Univ. of Waterloo (Canada)
Hermann Hubschle, Imaging Research Inc. (Canada)
Edward Susko, Dalhousie Univ. (Canada)
Nezar Rghei, Imaging Research Inc. (Canada)
Peter Ramm, Imaging Research Inc. and Brock Univ. (Canada)

Published in SPIE Proceedings Vol. 4266:
Microarrays: Optical Technologies and Informatics
Michael L. Bittner; Yidong Chen; Andreas N. Dorsel; Edward R. Dougherty, Editor(s)

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