Microarray experiments offer the ability to generate gene expression measurements for thousands of genes simultaneously. Work has begun recently on attempting to reconstruct genetic networks based on analyses of microarray experiments in time-course studies. An important tool in these analyses has been the singular value decomposition method. However, little work has been done on assessing the variability associated with singular value decomposition analyses. In this report, we discuss use of the bootstrap as a method of obtaining standard errors for singular value decomposition analyses. We consider use of this method both when there are replicates and when no replicates exist. The proposed methods are illustrated with an application to two datasets: one involving a human foreskin study, the other involving yeast
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