Method Statistics in Microarrays Experiment Analisis

Author(s)

Elier Broche Cristo

• Document type: Master's Dissertation
• Press: São Paulo.
• Institution: Universidade de São Paulo (USP). Instituto de Matemática e Estatística (IME/SBI)
• Defense date: 2003-10-30

Examining board members

Eduardo Jordao Neves; Nancy Lopes Garcia; Julia Maria Pavan Soler

• Advisor: Eduardo Jordao Neves

Abstract

In this work we propose a comparative study of some clustering methods (Hierarchic, K -Means and Self-Organizing Maps) and some classification methods (K-Neighbours, Fisher, Maximum Likelihood, Aggregating and Local Regression), which are presented teoretically. The methods are tested and compared based on the analysis of some real data sets, generated from Microarray experiments. This technique allows for the measurement of expression levels from thousands of genes simultaneously, thus allowing the comparative analysis of sample of tissues in relation to their expression profile. We present a review of basic concepts regarding normalization of microarray data, one of the first steps in microarray analysis. In particular, we were interested in finding small groups of genes that were ?sufficient? to identify samples originating from different biological conditions. Finally, a search method is proposed, which will find efficiently the best classifiers from the results of an experiment involving a huge number of genes. (AU)