Time Series Gene Expression Data Clustering and Pattern Extraction in Arabidopsis thaliana Phosphatase-encoding Genes

Clustering of genes using their expression data has been a major topic in recent years. A large amount of gene expression data even in time series are obtained by microarray technology. Finding gene clusters with similar functions and interconnecting genes by networks has an important role in mining biological gene functional analysis. In this paper, Two Phase Functional Clustering has been presented as a new approach in gene clustering. The proposed approach is based on finding functional patterns of time series gene expression data by Fuzzy C-Means (FCM) and K-means methods. The gene function similarities over a number of experimental conditions are extracted using Pearson correlation between expression patterns of genes. This leads to visualize genes interconnections.