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Robust test method for time-course microarray experiments.

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Date
2010-07-22
Authors
Sohn, Insuk
Owzar, Kouros
George, Stephen L
Kim, Sujong
Jung, Sin-Ho
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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.
Type
Journal article
Subject
Adult
Aged
Aged, 80 and over
Algorithms
Animals
Arthritis, Rheumatoid
Caenorhabditis elegans
Computer Simulation
Gene Expression Profiling
Humans
Middle Aged
Oligonucleotide Array Sequence Analysis
Regression Analysis
Time Factors
Young Adult
Permalink
https://hdl.handle.net/10161/4334
Published Version (Please cite this version)
10.1186/1471-2105-11-391
Publication Info
Sohn, Insuk; Owzar, Kouros; George, Stephen L; Kim, Sujong; & Jung, Sin-Ho (2010). Robust test method for time-course microarray experiments. BMC Bioinformatics, 11. pp. 391. 10.1186/1471-2105-11-391. Retrieved from https://hdl.handle.net/10161/4334.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
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Scholars@Duke

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 and Bioinformatics
Design of Clinical Trials Survival Analysis Longitudinal Data Analysis Clustered Data Analysis ROC Curve Analysis Design and Analysis of Microarray StudiesBig Data Analysis
Owzar

Kouros Owzar

Professor of Biostatistics & Bioinformatics
cancer pharmacogenomicsdrug induced neuropathy, neutropenia and hypertensionstatistical genetics statistical methods for high-dimensional data copulas survival analysis statistical computing
Alphabetical list of authors with Scholars@Duke profiles.
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