The networked partial correlation and its application to the analysis of genetic interactions
Genetic interactions confer robustness on cells in response to genetic perturbations. This often occurs through molecular buffering mechanisms that can be predicted by using, among other features, the degree of coexpression between genes, which is commonly estimated through marginal measures of asso...
| Autores: | , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2017 |
| País: | España |
| Recursos: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositório: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10230/32383 |
| Acesso em linha: | http://hdl.handle.net/10230/32383 http://dx.doi.org/10.1111/rssc.12166 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Concentration matrix Covariance decomposition Gene coexpression Partial correlation Undirected graphical model |
| Resumo: | Genetic interactions confer robustness on cells in response to genetic perturbations. This often occurs through molecular buffering mechanisms that can be predicted by using, among other features, the degree of coexpression between genes, which is commonly estimated through marginal measures of association such as Pearson or Spearman correlation coefficients. However, marginal correlations are sensitive to indirect effects and often partial correlations are used instead. Yet, partial correlations convey no information about the (linear) influence of the coexpressed genes on the entire multivariate system, which may be crucial to discriminate functional associations from genetic interactions. To address these two shortcomings, here we propose to use the edge weight derived from the covariance decomposition over the paths of the associated gene network. We call this new quantity the networked partial correlation and use it to analyse genetic interactions in yeast. |
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