Gene expression pattern analysis
Microarray technology provides approach to measure the expression levels of large number of genes simultaneously and to look insight into the transcriptional state of the cell. It can be used for searching for co-expressed genes under certain conditions. As such, it has become a powerful tool in genetic network research and functional genomics. Meanwhile, the technology produces large amounts of data and the data interpretation becomes a major bottleneck. In this study, public yeast gene expression data is analyzed by Principal Components Analysis (PCA), Hierarchical Clustering, Self Organizing Mapping (SOM) and Adaptive Resonance Theory 2 (ART-2). The four statistical methods are also applied to maize chloroplast protein expression data in greening process. PCA can reduce the dimensionality of data set. The first few components contain most variance in the data and represent meaningful expression patterns. ART-2, a neural network method is for the first time applied to gene expression analysis in our study. ART-2 provides very good clustering quality. Compared with Hierarchical Clustering and SOM, ART-2 is not limited by the rigid structure of Hierarchical Clustering and is not required to determine the clustering number in the beginning such as SOM. ART-2 has ability to deal with noise in the data and is easy to implement and interpret the result. The algorithm is also fast and scalable.