Robust Analysis Of DNA Microarray Data

• Activity Number: 350
• Type: Topic Contributed
• Date/Time: Wednesday, August 14, 2002 : 2:00 PM to 3:50 PM
• Sponsor: Section on Nonparametric Statistics*
• Author(s): Kimberly Crimin*+
• Affiliation(s): Western Michigan University
• Keywords: DNA microarrays ; robust methods

Abstract

Through the use of DNA microarrays, it is possible to study the changes in gene expression level of thousands of genes in different biological states. Microarrays give a genome-wide picture of the level of transcripts at a particular time and state of a cell production. But microarrays are sensitive to small fluctuations in transcript level; thus, changes in genetic background and variables such as culture conditions can introduce changes which may not be related to the experiment. Recognizing the fact that the data is "noisy," traditional methods may not be sensitive enough to detect small changes in biological states. Therefore, to detect differentially expressed genes in DNA microarray experiments, it is necessary to use statistical methods that are more robust than traditional methods. We will discuss several microarray experiments and the robust and traditional methods that we have used to conduct our statistical analysis. The robust methods were generally more successful in the analysis of the data. With the robust methods we were able to detect smaller changes in expression level than traditional least squares methods do.