Robust test method for time-course microarray experiments.
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BACKGROUND: In a time-course microarray experiment, the expression level for each gene is observed across a number of time-points in order to characterize the temporal trajectories of the gene-expression profiles. For many of these experiments, the scientific aim is the identification of genes for which the trajectories depend on an experimental or phenotypic factor. There is an extensive recent body of literature on statistical methodology for addressing this analytical problem. Most of the existing methods are based on estimating the time-course trajectories using parametric or non-parametric mean regression methods. The sensitivity of these regression methods to outliers, an issue that is well documented in the statistical literature, should be of concern when analyzing microarray data. RESULTS: In this paper, we propose a robust testing method for identifying genes whose expression time profiles depend on a factor. Furthermore, we propose a multiple testing procedure to adjust for multiplicity. CONCLUSIONS: Through an extensive simulation study, we will illustrate the performance of our method. Finally, we will report the results from applying our method to a case study and discussing potential extensions.
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Gene Expression Profiling
Oligonucleotide Array Sequence Analysis
Published Version (Please cite this version)10.1186/1471-2105-11-391
Publication InfoGeorge, Stephen L; Jung, SH; Kim, S; Owzar, Kouros; & Sohn, I (2010). Robust test method for time-course microarray experiments. BMC Bioinformatics, 11. pp. 391. 10.1186/1471-2105-11-391. Retrieved from http://hdl.handle.net/10161/4334.
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Professor Emeritus of Biostatistics and Bioinformatics
Statistical issues related to the design, conduct, and analysis of clinical trials and related biomedical studies including sample size and study length determinations, sequential procedures, and the analysis of prognostic or predictive factors in clinical trials.
Professor of Biostatistics and Bioinformatics
cancer pharmacogenomicsdrug induced neuropathy, neutropenia and hypertensionstatistical genetics statistical methods for high-dimensional data copulas survival analysis statistical computing
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