Abstract of the invited talk  

Web-based Gene Expression Data Analysis

With the advance of microarray technologies, biologists are currently capable of observing the abundance of transcripts from tens of thousands genes in biological samples, enabling the exploration of the dynamics of transcription and interaction between genes on a genome-wide scale. With the accumulation of gene expression dataset, the challenging task of all microarray experiments is how to extract meaningful and trustworthy information out of thousands of genes that do not contribute in the designed experiments. To achieve this goal, many rigorous mathematical tools and computational software were introduced to the field, such as statistical techniques for data normalization, clustering algorithms, class prediction methods, ANOVA, and gene-gene interaction studies. Realizing that many of gene expression experiments collect relatively small number of samples from patients, cell-lines, or other biological samples, rendering some of popular statistical tools meaningless, the development of data visualization techniques is also crucial in various stages of microarray experimen. To assist biologists to efficiently organize, and therefore, understand the properties of their dataset, we introduced and implemented the multidimensional scaling (MDS) technique to provide direct appreciation of the clustering outcome, various clustering techniques for data organizing and pattern finding purpose, techniques for visualizing gene-gene interaction via coefficient of determination (CoD), and many others. In this presentation, we will use some of the gene expression profile studies for various cancer samples in our lab to illustrate statistical tasks required in analyses, as well as the implementation of a web-based statistical and bioinformatics tool sets to provide various expression data normalization, filtering, visualization, clustering, gene selection and sample classification techniques. The unique web-based implementation with simple spread-sheet data format, allowing biologists with maximum flexibility to explore their datasets, in the meantime alleviates the burden of biological information collection as well as the burden of software distribution.