SAM method as an approach to select candidates for human prostate cancer markers

Authors:
A C Q Simoes;A M da Silva;S Verjovski-Almeida;E M Reis
Affiliations:
Departamento de Bioquímica, Instituto de Química;Departamento de Bioquímica, Instituto de Química;Departamento de Bioquímica, Instituto de Química;Departamento de Bioquímica, Instituto de Química
Venue:
BSB'05 Proceedings of the 2005 Brazilian conference on Advances in Bioinformatics and Computational Biology
Year:
2005

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Hierarchic document classification using Ward's clustering method

Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
Differential gene expression detection using penalized linear regression models: the improved SAM statistics

Bioinformatics

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Abstract

In order to select gene markers among differentially expressed transcripts identified from tumoral prostate, we have applied a filter and Significance Analysis of Microarrays (SAM) as the feature selection method on a previously normalized dataset of DNA microarray experiments reported by Reis et al., 2004 (Oncogene 23:6684-6692). Twenty seven samples with different degrees of tumor differentiation (Gleason scores) were analyzed. SAM was run using either two-class, unpaired data analysis with Gleason 5-6 and Gleason 9-10 samples, or multiclass response analysis with an additional category of Gleason 7-8. Both strategies revealed a promising set of transcripts associated with the degree of differentiation of prostate tumors.