Modeling considerations for using expression data from multiple species.
Stat Med
; 32(23): 4057-70, 2013 Oct 15.
Article
em En
| MEDLINE
| ID: mdl-23703923
ABSTRACT
Although genome-wide expression data sets from multiple species are now more commonly generated, there have been few studies on how to best integrate this type of correlated data into models. Starting with a single-species, linear regression model that predicts transcription factor binding sites as a case study, we investigated how best to take into account the correlated expression data when extending this model to multiple species. Using a multivariate regression model, we accounted for the phylogenetic relationships among the species in two ways (i) a repeated-measures model, where the error term is constrained; and (ii) a Bayesian hierarchical model, where the prior distributions of the regression coefficients are constrained. We show that both multiple-species models improve predictive performance over the single-species model. When compared with each other, the repeated-measures model outperformed the Bayesian model. We suggest a possible explanation for the better performance of the model with the constrained error term.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Filogenia
/
Modelos Lineares
/
Análise Multivariada
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Interpretação Estatística de Dados
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Teorema de Bayes
/
Perfilação da Expressão Gênica
Tipo de estudo:
Prognostic_studies
Limite:
Animals
/
Humans
Idioma:
En
Revista:
Stat Med
Ano de publicação:
2013
Tipo de documento:
Article
País de afiliação:
Estados Unidos