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Assessing Numerical Dependence in Gene Expression Summaries with the Jackknife Expression Difference

Statistical methods to test for differential expression traditionally assume that each gene's expression summaries are independent across arrays. When certain preprocessing methods are used to obtain those summaries, this assumption is not necessarily true. In general, the erroneous assumption...

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Detalles Bibliográficos
Autores principales: Stevens, John R., Nicholas, Gabriel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3411624/
https://www.ncbi.nlm.nih.gov/pubmed/22876276
http://dx.doi.org/10.1371/journal.pone.0039570
Descripción
Sumario:Statistical methods to test for differential expression traditionally assume that each gene's expression summaries are independent across arrays. When certain preprocessing methods are used to obtain those summaries, this assumption is not necessarily true. In general, the erroneous assumption of dependence results in a loss of statistical power. We introduce a diagnostic measure of numerical dependence for gene expression summaries from any preprocessing method and discuss the relative performance of several common preprocessing methods with respect to this measure. Some common preprocessing methods introduce non-trivial levels of numerical dependence. The issue of (between-array) dependence has received little if any attention in the literature, and researchers working with gene expression data should not take such properties for granted, or they risk unnecessarily losing statistical power.