Effect of data normalization on fuzzy clustering of DNA microarray data

Background: Microarray technology has made it possible to simultaneously measure the expression levels of large numbers of genes in a short time. Gene expression data is information rich; however, extensive data mining is required to identify the patterns that characterize the underlying mechanisms of action. Clustering is an important tool for finding groups of genes with similar expression patterns in microarray data analysis. However, hard clustering methods, which assign each gene exactly to one cluster, are poorly suited to the analysis of microarray datasets because in such datasets the clusters of genes frequently overlap. Results: In this study we applied the fuzzy partitional clustering method known as Fuzzy C-Means (FCM) to overcome the limitations of hard clustering. To identify the effect of data normalization, we used three normalization methods, the two common scale and location transformations and Lowess normalization methods, to normalize three microarray datasets and ...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Seo Young Kim, Jae Won Lee, Jong Sung Bae
• Keywords: BMCBI 2006, Lowess Normalization, Microarray Datasets, Normalization Method