Robust test method for time-course microarray experiments.

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2010-07-22

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Abstract

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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10.1186/1471-2105-11-391

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Sohn, Insuk, Kouros Owzar, Stephen L George, Sujong Kim and Sin-Ho Jung (2010). Robust test method for time-course microarray experiments. BMC Bioinformatics, 11. p. 391. 10.1186/1471-2105-11-391 Retrieved from https://hdl.handle.net/10161/4334.

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Scholars@Duke

Owzar

Kouros Owzar

Professor of Biostatistics & Bioinformatics

cancer pharmacogenomics
drug induced neuropathy, neutropenia and hypertension
statistical genetics
statistical methods for high-dimensional data
copulas
survival analysis
statistical computing

George

Stephen L. George

Professor Emeritus of Biostatistics & 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.

Jung

Sin-Ho Jung

Professor of Biostatistics & Bioinformatics

Design of Clinical Trials
Survival Analysis
Longitudinal Data Analysis
Clustered Data Analysis
ROC Curve Analysis
Design and Analysis of Microarray Studies
Big Data Analysis


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