STRUCTURAL VISUALIZATION IN BIOINFORMATICS

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. There has been no change in the support of key personnel since the last reporting period. The estimated unobligated balance will not be greater than 25% of the current year's budget. A. Specific Aims: The specific aims of this grant are as stated in the original proposal. B. Progress this year: The majority of the work in the final year of the grant was focused on Specific Aims 1D, 2A and 2B. Specific aim 1D refers to provided platform-wide analysis of microarray data in order to extract patterns. Specific aim 2A and Specific aim 2B refer to the development of web-based software tools to do structural analysis and visualization. We have developed several analysis techniques based on the cumulative distribution functions (CDF) of the microarray samples to detect outliers, unusual expression patterns, and expression patterns that are typical across all experiments. These methods are structural analysis techniques in that they analyze the CDFs against the background of the typical platform-wide CDF. We developed a method of calculating a "gene effect" which is a baseline expression level associated with each gene independent of experimental condition. These effects can only be calculated by looking at a large number of samples. Genes with high gene effect are likely to be housekeeping genes and so gene effect models provide an independent method of determining housekeeping genes. When looking for genes that are highly expressed, the novelty of the expression is determined to some extent by the value of its gene effect. We have shown that gene effects are consistent across platforms. These results have been reported in several technical reports and in 2 papers that are undergoing revision. We are working to show that gene effects play an important role in the analysis of differential expression and are applying these results in several large scale cancer microarray studies.