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Activity Number:
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471
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Type:
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Contributed
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Date/Time:
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Thursday, August 7, 2003 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #300347 |
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Title:
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Statistical Significance Analysis of Longitudinal Gene Expression Data
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Author(s):
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Xu Guo*+ and Huilin Qi and Catherine M. Verfaillie and Wei Pan
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Companies:
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University of Minnesota and University of Minnesota and University of Minnesota and University of Minnesota
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Address:
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1026 27th Ave. SE, Apt. A, Minneapolis, MN, 55414,
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Keywords:
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estimating equation ; microarray ; mixture model ; robust Wald statistic ; sandwich variance estimator ; Significance Analysis of Microarrays (SAM)
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Abstract:
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Longitudinal gene expression data arise from time-course microarray experiments, which are designed to study biological processes in a temporal fashion by taking samples from the same subject at different time points to measure gene expression levels. It is well-known in other applications that valid statistical analyses have to appropriately account for possible correlation in longitudinal data. For this reason, we apply estimating equation techniques to construct a robust statistic, which is a variant of the robust Wald statistic, for longitudinal gene expression data to detect genes with temporal changes in expression. We associate significance levels to the proposed statistic by either incorporating the idea of the Significance Analysis of Microarrays method or using the mixture model method to identify significant genes. The utility of the statistic is demonstrated through its application to an important study of osteoblast lineage-specific differentiation. Using simulated data, we also show pitfalls in drawing statistical inferences when the correlation in longitudinal gene expression data is ignored.
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