AEGS: identifying aberrantly expressed gene sets for differential variability analysis

作者：Guan, Jinting; Chen, Moliang; Ye, Congting; Cai, James J.; Ji, Guoli^*

来源：Bioinformatics, 2018, 34(5): 881-883.

DOI：10.1093/bioinformatics/btx646

In gene expression studies, differential expression (DE) analysis has been widely used to identify genes with shifted expression mean between groups. Recently, differential variability (DV) analysis has been increasingly applied as analyzing changed expression variability (e.g. the changes in expression variance) between groups may reveal underlying genetic heterogeneity and undetected interactions, which has great implications in many fields of biology. An easy-to-use tool for DV analysis is needed.
We develop AEGS for DV analysis, to identify aberrantly expressed gene sets in diseased cases but not in controls. AEGS can rank individual genes in an aberrantly expressed gene set by each gene's relative contribution to the total degree of aberrant expression, prioritizing top genes. AEGS can be used for discovering gene sets with disease-specific expression variability changes.
AEGS web server is accessible at http://bmi.xmu.edu.cn:8003/AEGS, where a stand-alone AEGS application can also be downloaded.

• 出版日期2018-3-1
• 单位厦门大学