Application of Coincidence Index in the Discovery of Co-Expressed Metabolic Pathways

Analyzing transcription data requires intensive statistical analysis in order to obtain useful biological information and knowledge. A significant portion of this data is affected by random noise or even noise intrinsic to the modeling of the experiment. Without a robust treatment, the data might not be very thoroughly explored and even incorrect conclusions could be drawn. Examining the correlation between gene expression profiles is one of the ways bioinformaticians extract information from transcriptomic experiments. However, the correlation measurements traditionally used have worrisome shortcomings that need to be addressed. This paper compares the two most common correlation measurements, Pearsons r and Spearmans r, to the newly developed coincidence index, a similarity measurement that combines Jaccard and interiority indexes and generalizes them to be applied to vectors containing real values. We used experimental data from a microarray experiment from the archaeon Halobacterium salinarum that evaluates the effects on the organism when exposed to light in an anaerobic environment. The utilized method explores the co-expressed metabolic pathways by measuring the correlations between enzymes that share metabolites and searches for local maxima using a simulated annealing algorithm. We demonstrate that the coincidence index extracts larger, more comprehensive, and more statistically significant pathways than the traditional Pearsons and Spearmans measurements.