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.

A Microarray is a small analytical device that allows genomic analysis with speed and precision. Through this technology the expression of thousands of genes can be simultaneously explored. Fundamental question about the Microarray data is how to choose the genetic markers. Detection of genetic markers, shall lead to early detection, treatment, and accurately prognosis; therefore, appropriate treatment and reduction of cost. Using a classical approach for Microarray data processing and statistical analysis, we are able to detect only two genes as differentially regulated in samples under investigation. Recently, in an effort to boost the biologically meaningful signal, game theory researchers, have been able to devise a method on gene expression data, Thus we were able to identify a group of genes associated with a disorder. This method, which is called comparative analysis of Shapley value, by combining the concept of Shapley value of cooperative games and statistical bootstrap lead to selection of genes with powerful influence in occurrence of a disorder. In this paper, this approach will be developed and in a case study of this modeling the results of applying this approach on gene expression data from autistic individuals, showed that coalitional games significantly increase the power to identify candidates.

Microarray technology is a powerful technique to measure the expression levels of large numbers of genes simultaneously. Microarray data contains many noise sources; therefore, several preprocessing steps are necessary to convert the raw data to achieve accurate analyzing results. Preprocessing of Microarray data includes background correction, data normalization, and summarization steps each can be performed by a large variety of methods. However, the relative impact of these methods on the detection of differentially expressed genes remains to be determined. The aim of this study was to compare the effects of different methods of preprocessing on the results of differentially expressed gene detection. The used data was downloaded from the NCBI GEO database. The series (GSE) accession number, platform (GPL) accession number, and platform name of the data were GSE56589, GPL18534, and Affymetrix Bovine Genome Array, respectively. Two background correction methods (MAS.5 and RMA.2), two normalization methods (Scaling normalization and Quantile normalization), and two summarization methods (Tukey biweight and Medianpolish) were evaluated. The results showed that the number and types of differentially expressed genes could be mainly affected by background correction and normalization methods, but the summarization method showed a small impact.

Introduction: Leukemia is a progressive and malignant disease of hematopoietic organs of the body. Genetic abnormalities play an important role in the development of leukemia in the body. Many studies have been accomplished on the molecular factors that involved in the disease. DNA Microarray technology provides a general picture of gene expression in whole genome and applied for the exploring of candidate genes that lead to diseases. In fact, having analyzed a large number of genes together along with the expected changes provides more closely examination the disease under study. In the present study, the DNA Microarray data of leukemia disease were analyzed by bioinformatics software (DAVID). The aim of the study was to functionally analyze the genomic and proteomic lists of data that have been obtained with high throughput tools during biological studies.Materials & Methods: Microarray leukemia gene sets were obtained from the database http: //www.biomedcentral.com/ content/ supplementary/1471and analyzed with the bioinformatics software (DAVID). The communication gene expression in different classes, chart and clustered genes were examined. The list of genes was identified by the DAVID analysis program.Findings: A chart consisting of 615 identified genes associated with various diseases was detected. Most genes involved in the disease were those genes that were also involved in cancers. 23.7% of the identified genes (146 genes) were cancer genes. Of 615 genes, 70 charts of the identified biological pathways (the database KEGG) were associated with the disease. Of 615 genes identified, 12 clusters were associated with the disease based on the functional annotation.Discussion & Conclusion: The results showed that the program, DAVID, is capable of analyzing genome. Also, the program was capable to evaluate the main classes of genes and pathways involved in the disease to determine the best candidate gene markers for the diagnosis and treatment of leukemia disease.

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.