Testing for Trends in Dose-Response Microarray Experiments: A Comparison of Several Testing Procedures, Multiplicity and Resampling-Based Inference

• Published in: Statistical Applications in Genetics and Molecular Biology Vol. 6; no. 1; pp. 26 - 53
• Main Authors: Lin, Dan, Shkedy, Ziv, Yekutieli, Dani, Burzykowski, Tomasz, Göhlmann, Hinrich W.H., De Bondt, An, Perera, Tim, Geerts, Tamara, Bijnens, Luc
• Format: Journal Article
• Language: English
• Published: Germany bepress 01.01.2007; De Gruyter
• Subjects: dose response study; Gene Library; Humans; Likelihood Functions; monotonicity; multiple testing; Oligonucleotide Array Sequence Analysis - methods; Psychological Tests; Reproducibility of Results; resampling-based procedures
• ISSN: 1544-6115; 2194-6302; 1544-6115
• DOI: 10.2202/1544-6115.1283

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.