Compressing and Analyzing Microarray Images for Genetic Information Extraction

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Bioinformatics is the application of information technology to study and analyze biological and genetic data. Functional genomics, the second phase of the Human Genome Project, assigns function to DNA sequences, and is gearing up rapidly based on the new microarray technology. Microarray images make possible the simultaneous expressions of thousands of genes, and hold the keys to understanding of gene regulation and interaction, genetic pathways for diseases such as cancer, and finding the subpopulations most responsive to certain drugs. They are widely used in laboratories of academia and industry alike. The emergence of the microarray imaging technology puts image processing in an important position in functional genomics, and calls for interdisciplinary research between image processing and statistical data analysis. The investigators will be studying problems at exactly this interface.The amount of data for even one microarray image is HUGE (30 MB/scan and two scans/image). The need for compression is pressing because of the exponential explosion of its use and (despite of the cheaper disk space) the bottleneck on the conversion cost to tapes, the only reliable permanent storage. The investigators are addressing this need for microarray image compression with a progressive or multi-level coded data structure which is useful for transmission and statistical analysis. A case is made for lossy compression to obtain aggressive compression ratios such as 15:1 without much loss of statistical information. This research is also investigating the optimality question of statistical estimation based lossy compressed data with applications to microarray data. Finally, this research is using the statistical modeling principle based on data compression: Rissanen's minimum description length (MDL) principle, for gene clustering and other biological information extraction.