Graph-theoretical algorithmic analysis of microarray data for identification of murine brain ethanol-regulated gene networks

Abstract

The application of graph-theoretic algorithms to microarray expression data can help screen for genes of interest and aid in the identification of putative biological networks. We applied such algorithms for both clique-centric clustering and topological analysis to two murine datasets: one with 43 LXS RI lines and one with 27 BXD RI lines. Each dataset contained a control group, which was administered saline, and a test group, which was given ethanol (1.8 g/kg). For both datasets, Affymetrix M430A 2.0 microarrays were used to measure mRNA expression in the prefrontal cortex (PFC) at four hours post-treatment. Each dataset was normalized using both RMA and S-scores. Shown here are results from the S-score LXS dataset.