Coex-rank: an approach for microarray combined analysis - applications to PPARγ related datasets

Microarrays have been widely used to study differential gene expression at the genomic level. They can also provide genome-wide co-expression information. Robust approaches are needed for integration and validation of independently-collected datasets which may contribute to a common hypothesis. Previously, attempts at meta-analysis have contributed to solutions to this problem. As an alternative, for microarray data from multiple highly similar biological experimental designs, a more direct combined approach is possible. In this thesis, a novel approach is described for microarray combined analysis, including gene-level unification into a virtual platform followed by normalization and a method for ranking candidate genes based on co-expression information – called Coex-Rank. We applied this approach to our Sppar (a PPARγ mutant) dataset, which illustrated an improvement in statistical power and a complementary advantage of the Coex-Rank method from a biological perspective. We also performed analysis to other PPARγ-related microarray datasets. From the perspective of gene sets, we observed that up-regulated genes from mice treated with the PPARγ ligand rosiglitazone were significantly down-regulated in mice with a global knock-in dominant-negative mutation of PPARγ. Integrated with publicly available PPRE (PPAR Response Element) datasets, we found that the genes which were most upregulated by rosiglitazone treatment and which were also down-regulated by the global knock-in mutation of PPARγ were robustly enriched in PPREs near transcription start sites. In addition, we identified several potential PPARγ targets in the aorta and mesenteric artery for further experimental validation, such as Rhobtb1 and Rgs5.