| Recent advances in high throughput biological methods allow researchers to generate enormous amounts of data from a single experiment. In order to extract meaningful conclusions from this tidal wave of data, it will be necessary to develop analytical methods of sufficient power and utility. It is particularly important that biologists themselves perform many of these analyses, such that their background knowledge of the experimental system under study can be used to interpret results and direct further inquiries.; This dissertation describes the development of a web-based system, Magellan, which allows the upload, storage, and analysis of multivariate data and textual or numeric annotations. Data and annotations are treated as abstract entities, to maximize the different types of information the system can store and analyze. Annotations can be used in analyses/visualizations, as a means of sub setting data to reduce dimensionality, or as a means of projecting variables from one data type or data set to another. Analytical methods are deployed within Magellan such that new functionalities can be added in a straightforward fashion.; The Magellan system has been used to analyze a number of cancer genomics data sets. These analyses have involved the development and deployment of a number of analytical methods that relate different types of genomic variables, typically comparative genomic hybridization (CGH), mRNA expression and clinical information. In addition, I have worked with the National Cancer Institute on the Cancer Bioinformatics Grid (caBIG) initiative, to develop and deliver the functionality of Magellan as an open source project available to any researcher. |
