Two-way gene interaction from microarray data based on correlation methods

Background: Gene networks have generated a massive explosion in the development of high-throughput techniques for monitoring various aspects of gene activity.Networks offer a natural way to model interactions between genes,and extracting gene network information from high-throughput genomic data is an important and difficult task.Objectives: The purpose of this study is to construct a two-way gene network based on parametric and nonparametric correlation coefficients.The first step in constructing a Gene Co-expression Network is to score all pairs of gene vectors.The second step is to select a score threshold and connect all gene pairs whose scores exceed this value.Materials and Methods: In the foundation-application study,we constructed two-way gene networks using nonparametric methods,such as Spearman’s rank correlation coefficient and Blomqvist’s measure,and compared them with Pearson’s correlation coefficient.We surveyed six genes of venous thrombosis disease,made a matrix entry representing the score for the corresponding gene pair,and obtained two-way interactions using Pearson’s correlation,Spearman’s rank correlation,and Blomqvist’s coefficient.Finally,these methods were compared with Cytoscape,based onBIND,and Gene Ontology,based onmolecular function visual methods;R software version 3.2 and Bioconductor were used to perform these methods.Results: Basedonthe Pearson and Spearman correlations,the results were thesameand wereconfirmedby Cytoscape andGOvisual methods;however,Blomqvist’s coefficient was not confirmed by visual methods.Conclusions: Some results of the correlation coefficients are not the same with visualization.The reason may be due to the small number of data.© 2016,Iranian Red Crescent Medical Journal.

ارسال دیدگاه

نشانی ایمیل شما منتشر نخواهد شد. بخش‌های موردنیاز علامت‌گذاری شده‌اند *