A FC-GSEA Approach to Identify Significant Gene-Sets Using Microarray Gene Expression Data - Advances in Computational Science and Engineering - page 115

A FC-GSEA Approach to Identify Significant Gene-Sets
Using Microarray Gene Expression Data
Jaeyoung Kim 1 and Miyoung Shin 2,*
1 Graduate School of Electrical Engineering and Computer Science, Kyungpook National
University, Daegu, South Korea 702-701
[e-mail]
2 School of Electrical Engineering and Computer Science, Kyungpook National University,
Daegu, South Korea 702-701
[e-mail]
Abstract. Gene set enrichment analysis (GSEA) is a computational method to
identify statistically significant gene-sets showing differential expression be-
tween 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 bio-
logical knowledge along with gene expression profiles during the analysis pro-
cedure. 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 in-
curred 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 em-
ployed for gene ranking instead of the signal-to-noise ratio, and evaluate its
effects made in Leukemia related pathway analyses.
Keywords: significant pathway, gene set enrichment analysis, gene ranking,
Fisher's criterion, microarray data analysis.
1 Introduction
Recent advance in microarray gene expression studies has played an important role in
elucidating and understanding complicated biological phenomenon occurred in vari-
ous organisms by performing computational analysis on gene expression profiles
along with a variety of biological resources [1], [8], [9]. In particular, one of the im-
portant challenges in gene expression studies is to identify differentially regulated
genes between two groups and understand their biological meanings. For this purpose,
the gene set enrichment analysis (GSEA) [1] has been lately developed as a unified
analytical framework that employs a priori known biological resources with gene
* Corresponding author.