TY - GEN
T1 - A FC-GSEA approach to identify significant gene-sets using microarray gene expression data
AU - Kim, Jaeyoung
AU - Shin, Miyoung
PY - 2009
Y1 - 2009
N2 - Gene set enrichment analysis (GSEA) is a computational method to identify statistically significant gene-sets showing differential expression between two groups. In particular, unlike other previous approaches, it enables us to uncover the biological meanings of the identified gene-sets in an elegant way by providing a unified analytical framework that employs a priori known biological knowledge along with gene expression profiles during the analysis procedure. For original GSEA, all the genes in a given dataset are ranked by the signal-to-noise ratio of their microarray expression profiles between two groups and then further analyses are proceeded. Despite of its impressive results in previous studies, however, the gene ranking by the signal-to-noise ratio makes it hard to consider both highly up-regulated genes and highly down-regulated genes at a time as significant genes, which may not reflect such situations as incurred in metabolic and signaling pathways. To deal with this problem, in this article, we investigate the FC-GSEA method where the Fisher's criterion is employed for gene ranking instead of the signal-to-noise ratio, and evaluate its effects made in Leukemia related pathway analyses.
AB - Gene set enrichment analysis (GSEA) is a computational method to identify statistically significant gene-sets showing differential expression between two groups. In particular, unlike other previous approaches, it enables us to uncover the biological meanings of the identified gene-sets in an elegant way by providing a unified analytical framework that employs a priori known biological knowledge along with gene expression profiles during the analysis procedure. For original GSEA, all the genes in a given dataset are ranked by the signal-to-noise ratio of their microarray expression profiles between two groups and then further analyses are proceeded. Despite of its impressive results in previous studies, however, the gene ranking by the signal-to-noise ratio makes it hard to consider both highly up-regulated genes and highly down-regulated genes at a time as significant genes, which may not reflect such situations as incurred in metabolic and signaling pathways. To deal with this problem, in this article, we investigate the FC-GSEA method where the Fisher's criterion is employed for gene ranking instead of the signal-to-noise ratio, and evaluate its effects made in Leukemia related pathway analyses.
KW - Fisher's criterion
KW - Gene ranking
KW - Gene set enrichment analysis
KW - Microarray data analysis
KW - Significant pathway
UR - http://www.scopus.com/inward/record.url?scp=73349103764&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-10238-7_10
DO - 10.1007/978-3-642-10238-7_10
M3 - Conference contribution
AN - SCOPUS:73349103764
SN - 9783642102370
T3 - Communications in Computer and Information Science
SP - 115
EP - 128
BT - Advances in Computational Science and Engineering
A2 - Kim, Tai-hoon
A2 - Yang, Laurence T.
A2 - Park, Jong Hyuk
A2 - Chang, Alan Chin-Chen
A2 - Vasilakos, Thanos
A2 - Zhang, Yan
A2 - Sauveron, Damien
A2 - Wang, Xingang
A2 - Jeong, Young-Sik
ER -