Fuzzy Clustering Approach in DNA-Microarray

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. 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 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 group, which had been in AML group in clinical test. 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 dont have accurate information of real data grouping. Moreover, results of clustering might distinguish subgroups of data in such a way