Bioinformatics Vol. 19 no. 13 2003
Pages 1628-1635
© 2003 Oxford University Press
Statistical significance analysis of longitudinal gene expression data
1 Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, MMC 303, Minneapolis, MN 55455-0378, USA and 2 Stem Cell Institute, Department of Medicine, University of Minnesota Medical School, 422 Delaware Street SE, MMC 716, Minneapolis, MN 55445, USA
Received on October 17, 2002
; revised on November 2, 2002
; accepted on March 11, 2003
Motivation: Time-course microarray experiments are designed to study biological processes in a temporal fashion. Longitudinal gene expression data arise when biological samples taken from the same subject at different time points are used to measure the gene expression levels. It has been observed that the gene expression patterns of samples of a given tumor measured at different time points are likely to be much more similar to each other than are the expression patterns of tumor samples of the same type taken from different subjects. In statistics, this phenomenon is called the within-subject correlation of repeated measurements on the same subject, and the resulting data are called longitudinal data. It is well known in other applications that valid statistical analyses have to appropriately take account of the possible within-subject correlation in longitudinal data.
Results: We apply estimating equation techniques to construct a robust statistic, which is a variant of the robust Wald statistic and accounts for the potential within-subject correlation of 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 by applying it to an important study of osteoblast lineage-specific differentiation. Using simulated data, we also show pitfalls in drawing statistical inference when the within-subject correlation in longitudinal gene expression data is ignored.
Contact: weip{at}biostat.umn.edu
* To whom correspondence should be addressed.
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