Microarray technology has a wide usage in the fields of genomics and proteomics and enables us to monitor innumerable biological interactions simultaneously. This technology is very efficient and analyzes a lot of data in a short interval. The findings of Microarray are then processed analyzed using bioinformatics. The basis of all Microarrays is similar: there is a solid surface on which specific ligands are spotted. Then, a biological solution is poured on the surface and based on the binding of the ligand to the specific substance, the results are analyzed.

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 & Aim: Microarray techniques are successfully used to investigate thousands of gene expression profiling in a variety of genomic analyses such as gene identification, drug discovery and clinical diagnosis, providing a large amount of genomic data for the overall research community. Statistical analysis of such databases included normalization, clustering, classification, etc. The present study surveyed the application of fuzzy clustering technique in DNA Microarray analysis. Materials & Methods: Golub, et al collected data bases of leukemia based on the method of oligonucleotide in 1999. The data are on the internet for free. In this paper we did analysis on this data set and gene expression data were clustered by fuzzy clustering. Data set included 20 Acute Lymphoblastic Leukemia (ALL) patients and 14 Acute Myeloid Leukemia (AML) patients. Efficiency of clustering was compared with regard to real grouping (ALL & AML). We used R software for data analysis.Results: Specificity and sensitivity of fuzzy clustering in diagnosing of ALL patients are 90% and 93%, respectively. These results show a good accomplishment of both clustering methods. It is considerable that, due to clustering methods results, one of the samples was placed in ALL groups, which had been in AML group in clinical test. Conclusion: With regard to concordance of the results with real grouping of data, it could be said that we can use these methods in cases where we don't have accurate information of real data grouping. Moreover, results of clustering might distinguish subgroups of data in such a way.

Background: Microarray DNA technology has paved the way for investigators to expressed thousands of genes in a short time. Analysis of this big amount of raw data includes normalization, clustering and classification. The present study surveys the application of clustering technique in Microarray DNA analysis. Materials and methods: We analyzed data of Van’t Veer et al study dealing with BRCA1 and BRCA2 mutations in breast cancer. It was consisted of 18 patients with BRCA1 and 2 patients with BRCA2 mutation. Gene expression data were clustered using hierarchical and non-hierarchical approach. Then different clustering approaches were compared according to the actual classification with R software. Results: Hierarchical clustering showed a sensitivity of 94% and specificity of 100% in detecting BRCA1 gene. These figures were 89% and 100% for non-hierarchical clustering, respectively, indicating a satisfactory performance for both approaches. All clustering approaches classified sample No. 95 in BRCA2 group, however, clinical manifestations put her in BRCA1 group. Conclusion: With respect to satisfactory coincidence between clustering and actual classification results, clustering approach could be applied for cases when actual classification is missing.