Roverato, AlbertoCastelo Valdueza, Robert2017-06-202017-06-202017Roverato A, Castelo Valdueza R. The networked partial correlation and its application to the analysis of genetic interactions. Journal of the Royal Statistical Society: Series C. 2017; 66(3): 647-665. DOI: 10.1111/rssc.121660035-9254http://hdl.handle.net/10230/32383Genetic 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.application/pdfeng© 2016 The Authors Journal of the Royal Statistical Society: Series C Applied Statistics published by John Wiley & Sons Ltd on behalf of Royal Statistical Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. http://dx.doi.org/10.1111/rssc.12166.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1111/rssc.12166Concentration matrixCovariance decompositionGene coexpressionPartial correlationUndirected graphical modelinfo:eu-repo/semantics/openAccess