Applications of correspondence analysis in microarray data analysis.

Date

2008-12-08T22:15:26Z

Authors

Mu, Ruixia

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Abstract

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.

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Keywords

Gene expressions, Variables, Microarray

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