A statistical framework for differential network analysis from microarray data

Background: It has been long well known that genes do not act alone; rather groups of genes act in consort during a biological process. Consequently, the expression levels of genes are dependent on each other. Experimental techniques to detect such interacting pairs of genes have been in place for quite some time. With the advent of microarray technology, newer computational techniques to detect such interaction or association between gene expressions are being proposed which lead to an association network. While most microarray analyses look for genes that are differentially expressed, it is of potentially greater significance to identify how entire association network structures change between two or more biological settings, say normal versus diseased cell types. Results: We provide a recipe for conducting a differential analysis of networks constructed from microarray data under two experimental settings. At the core of our approach lies a connectivity score that represents the st...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Ryan Gill, Somnath Datta, Susmita Datta
• Keywords: Association Network, BMCBI 2010, Connectivity Scores, Entire Association Network