A global picture of gene expression in the common immune-mediated skin disease, psoriasis, was obtained by interrogating the full set of Affymetrix GeneChips with psoriatic and control skin samples. We identified 1,338 genes with potential roles in psoriasis pathogenesis/maintenance and revealed many perturbed biological processes. A novel method for identifying transcription factor binding sites was also developed and applied to this dataset. Many of the identified sites are known to be involved in immune response and proliferation. An in-depth study of immune system genes revealed the presence of many regulating cytokines and chemokines within involved skin, and markers of dendritic cell (DC) activation in uninvolved skin. The combination of many CCR7+ T cells, DCs, and regulating chemokines in psoriatic lesions, together with the detection of DC activation markers in nonlesional skin, strongly suggests that the spatial organization of T cells and DCs could sustain chronic T-cell activation and persistence within focal skin regions.

Current methods for the functional analysis of microarray gene expression data make the implicit assumption that genes with similar expression profiles have similar functions in cells. However, among genes involved in the same biological pathway, not all gene pairs show high expression similarity. Here, we propose that transitive expression similarity among genes can be used as an important attribute to link genes of the same biological pathway. Based on large-scale yeast microarray expression data, we use the shortestpath analysis to identify transitive genes between two given genes from the same biological process. We find that not only functionally related genes with correlated expression profiles are identified but also those without. In the latter case, we compare our method to hierarchical clustering, and show that our method can reveal functional relationships among genes in a more precise manner. Finally, we show that our method can be used to reliably predict the function of unknown genes from known genes lying on the same shortest path. We assigned functions for 146 yeast genes that are considered as unknown by the Saccharomyces Genome Database and by the Yeast Proteome Database. These genes constitute around 5% of the unknown yeast ORFome.

The rapid accumulation of microarray data translates into a need for methods to effectively integrate data generated with different platforms. Here we introduce an approach, 2(nd)-order expression analysis, that addresses this challenge by first extracting expression patterns as meta-information from each data set (1(st)-order expression analysis) and then analyzing them across multiple data sets. Using yeast as a model system, we demonstrate two distinct advantages of our approach: we can identify genes of the same function yet without coexpression patterns and we can elucidate the cooperativities between transcription factors for regulatory network reconstruction by overcoming a key obstacle, namely the quantification of activities of transcription factors. Experiments reported in the literature and performed in our lab support a significant number of our predictions.