Data/hora: 15/06/2024 00:18:19

Provedor de dados: | 74
País: | Brazil
Título: | The best of both worlds: Phylogenetic eigenvector regression and mapping
Autores: | Diniz Filho,José Alexandre Felizola; Villalobos,Fabricio; Bini,Luis Mauricio.
Data: | 2015-09-01
Ano: | 2015
Palavras-chave: | Evolutionary models; Phylogenetic comparative methods; Phylogenetic imputation; Phylogenetic signal.
Resumo: | Eigenfunction analyses have been widely used to model patterns of autocorrelation in time, space and phylogeny. In a phylogenetic context, Diniz-Filho et al. (1998) proposed what they called Phylogenetic Eigenvector Regression (PVR), in which pairwise phylogenetic distances among species are submitted to a Principal Coordinate Analysis, and eigenvectors are then used as explanatory variables in regression, correlation or ANOVAs. More recently, a new approach called Phylogenetic Eigenvector Mapping (PEM) was proposed, with the main advantage of explicitly incorporating a model-based warping in phylogenetic distance in which an Ornstein-Uhlenbeck (O-U) process is fitted to data before eigenvector extraction. Here we compared PVR and PEM in respect to estimated phylogenetic signal, correlated evolution under alternative evolutionary models and phylogenetic imputation, using simulated data. Despite similarity between the two approaches, PEM has a slightly higher prediction ability and is more general than the original PVR. Even so, in a conceptual sense, PEM may provide a technique in the best of both worlds, combining the flexibility of data-driven and empirical eigenfunction analyses and the sounding insights provided by evolutionary models well known in comparative analyses.
Tipo: | Info:eu-repo/semantics/report
Idioma: | Inglês
Identificador: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-47572015000300396
Editor: | Sociedade Brasileira de Genética
Relação: | 10.1590/S1415-475738320140391
Formato: | text/html
Fonte: | Genetics and Molecular Biology  v.38 n.3 2015
Fonte: | info:eu-repo/semantics/openAccess