Normalization process is a set of operations whereby systematic biases are removed from Microarray data. Therefore researcher can attain acceptable results and have more logic comparisons. This process considering the bias constructions and the effects on Microarray is recognizable and applicable. Examples of this process are spatial correction, background correction, rescaling, dye effect and within slide normalization. Spot intensity standard deviation is decreased by that normalization processes, and confidence to the Microarray data analysis outcomes is increased. This paper describes all of these methods. In addition it contains the examples of a real dual channel cDNA Microarray experiment to illustrate normalization process.

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 the same and wereconfirmedby Cytoscape and GOvisual 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.