Resampling methods for variance estimation of singular value decomposition analyses from microarray experiments.
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.
Ghosh
Debashis
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Department of Biostatistics, School of Public Health, University of Michigan, 1420 Washington Heights, Room M4057, Ann Arbor 48109-2029, USA. ghoshd@umich.edu
D000465:Algorithms; D003627:Data Interpretation, Statistical; D005069:Evaluation Studies as Topic; D005347:Fibroblasts; D020869:Gene Expression Profiling; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results; D012984:Software; D015003:Yeasts / Q000235:genetics