Um método multiestatístico para identificação de vias genéticas diferencialmente expressas

Detalhes bibliográficos
Ano de defesa: 2016
Autor(a) principal: Fontoura, Carla Adriane Ramos Segatto
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de Santa Maria
BR
Física
UFSM
Programa de Pós-Graduação em Física
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: http://repositorio.ufsm.br/handle/1/3937
Resumo: The determination of the causes and origins of a given disease is a complex undertaking, considering that there is a large number of genes engaged that interact with each other (Watson, 2006). Bioinformatics experts working in the search for a perfect integration between biology and information, in order to understand the likely factors that trigger certain diseases (Pevzner, 2000). To achieve this, the revolutionary methodology of Microarrays (LOCKHART et al., 1996) based on the gene expression of patients, it has been widely used to simultaneously measuring changes and regulation of the genes of the genome under certain biological conditions, resulting in a list of genes that may be considered interesting from a biological point of view for a particular disease. In this thesis, we present a multi-statistic method to detect differentially expressed genetic pathways in DNA microarray data. Many statistical methods of analysis are based on the use of a single statistical test. It is believed that the use of multiple tests decreases the number of false positive discoveries. Our method can be applied to transcriptome data to investigate which pathways have changes in expression when subjected to some type of disturbance. The method determines the activity of pathways evaluated, and verifies if the changes found are statistically significant through the bootstrap, Fisher exact and Wilcoxon tests. Implemented in R language and available for download from the Comprehensive R Archive Network (CRAN) as a package called PATHChange, our method showed consistency in its results with those predicted in the literature when tested for microarray of cancer and pre-cancer colon public data. The PATHChange method offers an alternative type of analysis of differentially expressed genes pathways for researchers seeking to determine phenotypes of diseases such as cancer.