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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/8015

Title: Testing for trends in dose-response microarray experiments: A comparison of several testing procedures, multiplicity and resampling-based inference
Authors: LIN, Dan
SHKEDY, Ziv
Yekutieli, Dani
BURZYKOWSKI, Tomasz
Goehlmann, Hinrich W. H.
De Bondt, An
Perera, Tim
Geerts, Tamara
Bijnens, L.
Issue Date: 2007
Publisher: BERKELEY ELECTRONIC PRESS
Citation: STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, 6(1)
Abstract: Dose-response studies are commonly used in experiments in pharmaceutical research in order to investigate the dependence of the response on dose, i.e., a trend of the response level toxicity with respect to dose. In this paper we focus on dose-response experiments within a microarray setting in which several microarrays are available for a sequence of increasing dose levels. A gene is called differentially expressed if there is a monotonic trend (with respect to dose) in the gene expression. We review several testing procedures which can be used in order to test equality among the gene expression means against ordered alternatives with respect to dose, namely Williams' (Williams 1971 and 1972), Marcus' (Marcus 1976), global likelihood ratio test (Bartholomew 1961, Barlow et al. 1972, and Robertson et al. 1988), and M (Hu et al. 2005) statistics. Additionally we introduce a modification to the standard error of the M statistic. We compare the performance of these five test statistics. Moreover, we discuss the issue of one-sided versus two-sided testing procedures. False Discovery Rate (Benjamni and Hochberg 1995, Ge et al. 2003), and resampling-based Familywise Error Rate (Westfall and Young 1993) are used to handle the multiple testing issue. The methods above are applied to a data set with 4 doses (3 arrays per dose) and 16,998 genes. Results on the number of significant genes from each statistic are discussed. A simulation study is conducted to investigate the power of each statistic. A R library IsoGene implementing the methods is available from the first author.
Notes: Hasselt Univ, Diepenbeek, Belgium. Tel Aviv Univ, Tel Aviv, Israel.Lin, D, Hasselt Univ, Diepenbeek, Belgium.dan.lin@uhasselt.be ziv.shkedy@uhasselt.be yekutiel@post.tau.ac.il tomasz.burzykowski@uhasselt.be hgoehlma@prdbe.jnj.com adbondt@prdbe.jnj.com tperera@prdbe.jnj.com tgeerts@prdbe.jnj.com lbijnens@prdbe.jnj.com
URI: http://hdl.handle.net/1942/8015
Link to publication: http://www.bepress.com/sagmb/vol6/iss1/art26
ISI #: 000252387100001
ISSN: 1544-6115
Category: A1
Type: Journal Contribution
Validation: ecoom, 2009
Appears in Collections: Research publications

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