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Analysis of microarray data for treated fat cells Nicoleta Serban ∗ Larry Wasserman ∗, David Peters

By Peter Spirtes, Robert O’doherty Dan H, Richard Scheines and Clark Glymour

Abstract

DNA microarrays are perfectly suited for comparing gene expression in different populations of cells. An important application of microarray techniques is identifying genes which are activated by a particular drug of interest. This process will allow biologists to identify therapies targeted to particular diseases, and, eventually, to gain more knowledge about the biological processes in organisms. Such an application is described in this paper. It is focused on diabetes and obesity, which is a genetically heterogeneous disease, meaning that multiple defective genes are responsible for the diseases. The paper is divided in three parts, each dealing with a different problem addressed to our study. First we validate the data from our microarray experiment. We identified significant systematic sources of variability which are potentially issues for other microarray datasets. Second, we applied multiple hypothesis testing to identify differentially expressed genes. We found a set of genes which appear to change in expression level over time in response to a drug treatment. Third, we tried to address the problem of identification of co-expressed genes using cluster analysis. This last problem is still under discussion.

Year: 2003
OAI identifier: oai:CiteSeerX.psu:10.1.1.134.7028
Provided by: CiteSeerX
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