Correlation networks: Biologically driven relationships from gene expression data

Genes that share transcription factors are biologically driven to show a more likely measurable correlation in their gene expression. No modern method of visualization displays these intricate co-expression and correlation patterns better than a graph. Structural observations about a co-expression graph can reveal the secrets of the biological system that it models, but experimentally validated co-expression graphs are pain-staking work to produce. Present day correlation network analysis shows potential for drawing conclusions from large volumes of biological systems data in an inexpensive and easy-to-produce way; however, work remains to confirm the appropriateness and scope of such methods for specific, scientific application. Toward this effort, we generated a Pearson correlation network from gene expression data available to the public from the National Center for Biotechnology Information's Gene Expression Omnibus repository. From this dataset, we predicted shared transcription factor regulation among cliques of genes sharing upstream genomic motifs. Finally, our predictions, and thus the model itself, was contrasted against experimentally confirmed gene co-regulation data. Our process tested the hypothesis that the incorporation of correlation networks can enhance the prediction of transcription factor co-regulation from gene expression and upstream sequence data. Ultimately, our experimental results did not show a larger portion of true positive results when compared to a randomized control. These initial results indicate that correlation networks may not be an appropriate outlet for detecting co-expression motifs. Work remains to see if correlation networks can be constructed and normalized in a way that brings them closer to representing true co-expression.