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Identifying statistically significant gene sets based on differential expression and differential coexpression
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 Title & Authors
Identifying statistically significant gene sets based on differential expression and differential coexpression
Lee, Sunho;
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 Abstract
Gene set analysis utilizing biologic information is expected to produce more interpretable results because the occurrence of tumors (or diseases) is believed to be associated with the regulation of related genes. Many methods have been developed to identify statistically significant gene sets across different phenotypes; however, most focus exclusively on either the differential gene expression or the differential correlation structure in the gene set. This research provides a new method that simultaneously considers the differential expression of genes and differential coexpression with multiple genes in the gene set. Application of this NEW method is illustrated with real microarray data example, p53; subsequently, a simulation study compares its type I error rate and power with GSEA, SAMGS, GSCA and GSNCA.
 Keywords
microarray experiment;gene set analysis;differential expression;differential coexpression;
 Language
Korean
 Cited by
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