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Identifying Statistically Significant Gene-Sets by Gene Set Enrichment Analysis Using Fisher Criterion  

Kim, Jae-Young (Graduate School of Information and Communication Engineering, Kyungpook National University)
Shin, Mi-Young (School of Electrical and Engineering and Computer Science, Kyungpook National University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.45, no.4, 2008 , pp. 19-26 More about this Journal
Abstract
Gene set enrichment analysis (GSEA) is a computational method to identify statistically significant gene sets showing significant differences between two groups of microarray expression profiles and simultaneously uncover their biological meanings in an elegant way by employing gene annotation databases, such as Cytogenetic Band, KEGG pathways, gene ontology, and etc. For the gone set enrichment analysis, all the genes in a given dataset are first ordered by the signal-to-noise ratio between the groups and then further analyses are proceeded. Despite of its impressive results in several previous studies, however, gene ranking by the signal-to-noise ratio makes it difficult to consider highly up-regulated genes and highly down-regulated genes at the same time as the candidates of significant genes, which possibly reflect certain situations incurred in metabolic and signaling pathways. To deal with this problem, in this article, we investigate the gene set enrichment analysis method with Fisher criterion for gene ranking and also evaluate its effects in Leukemia related pathway analyses.
Keywords
Significant gene-sets; Gene Set Enrichment Analysis; Gene ranking; Fisher Criterion;
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