The management and analysis of the data produced by high-throughput technologies are challenging. This paper discuss multiple hypothesis testing by focusing on developing and applying computationally intensive techniques to achieve the goal of simultaneous tests for each spotID the null hypothesis of no association between the expression levels and the responses or covariates. The software provides features where the user controls the amount of data that can be used for analyzing. The software also produce graph of the output which provide the user with easy viewing of the results.
Library of Congress Subject Headings
Gene expression--Data processing; DNA microarrays; Genomics; Proteomics
Department, Program, or Center
Thomas H. Gosnell School of Life Sciences (COS)
Kannan, Anusha Aiyalu, "Detecting relevant changes in high throughput gene expression data" (2008). Thesis. Rochester Institute of Technology. Accessed from
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