Correspondence analysis is a descriptive and explorative technique for the study of associations
between variables. It is a visualization method for analyzing high dimensional
data via projections onto a low-dimensional subspace. In this thesis, we demonstrate
the applicability of correspondence analysis to microarray data. We show that it can be
used to identify important genes and treatment patterns by coordinating and projecting
the genes and the experimental conditions. In addition, we estimate missing values
in the gene expressions using the Expectation-Maximization (EM) algorithm and identify
genes with large between-condition variability using the projections of the genes
and the conditions. To demonstrate its application, correspondence analysis is applied
to various simulated data and microarray data from the EPA (Environmental Protection
Agency) studies. We conclude that correspondence analysis is a useful tool for
analyzing the associations between genes and experimental conditions, for identifying
important genes, and for estimating missing values.
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/1280 |
| Date | 08 December 2008 |
| Creators | Mu, Ruixia |
| Contributors | Lesperance, M. L. |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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