Comparison Analysis of the Pearson’s Phi-Square Test and Correlation Metric Effectiveness to Form the Subset of Differently Expressed and Mutually Correlated Genes

Abstract

The development of patients' health monitoring systems based on gene expression data is a very important direction of current bioinformatics. In this instance, the allocation of both differently expressed and mutually correlated gene expression profiles (GEP) which allow monitoring in real-time the patients' health with high accuracy is a very important step of this problem solution. There are various types of similarity metrics to identify the level of GEP proximity. In this research, we compare the Pearson chi-square test and correlation metric to evaluate the gene expression profiles proximity. The evaluation of appropriate metric effectiveness has been executed by applying the object's classification quality criteria such as accuracy, f-score and Matthews correlation coefficient (MCC). The simulation results have shown that the metric based on Pearson’s phi-square coefficient is significantly effective in comparison with the correlation metric to allocate the mutually similar gene expression profiles and, this metric can be used when the differently expressed and mutually correlated GEP will be extracted using various clustering algorithms.