Method Statistics in Microarrays Experiment Analisis / Métodos estatísticos na análise de experimentos de microarray

AUTOR(ES)

Elier Broche Cristo

DATA DE PUBLICAÇÃO

2003

RESUMO

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.

ASSUNTO(S)

análise estatística analisis statistics microarrays microarrays

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