Gene coexpression is a common feature employed in predicting buffering relationships that explain genetic interactions , which constitute an important mechanism behind the robustness of cells to genetic perturbations. The complete removal of such buffering connections impacts the entire molecular circuitry, ultimately leading to cellular death. Coexpression is commonly measured through Pearson correlation coefficients. However, Pearson correlation values are sensitive to indirect effects and often partial correlations are used instead. Yet, partial correlation values convey no information on the (linear) influence of the association within the entire multivariate system or, in other words, of the represented edge within the entire network. Jones and West (2005) showed that covariance can be decomposed into the weights of the paths that connect two variables within the corresponding undirected network. Here we provide a precise interpretation of path weights and show that, in the particular case of single-edge paths, this interpretation leads to a quantity we call networked partial correlation whose value depends on both the partial correlation between the intervening variables and their association with the rest of the multivariate system. We show that this new quantity correlates better with quantitative genetic interactions in yeast than classical coexpression measures .
Genetic interactions confer robustness on cells in response to genetic perturbations. This often occurs through molecular buffering mechanisms that can be predicted using, among other features, the degree of coexpression between genes, 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 analyze genetic interactions in yeast .
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Gene coexpression is a common feature employed in predicting buffering relationships that explain genetic interactions , which constitute an important mechanism behind the robustness of cells
Genetic interactions confer robustness on cells in response
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The complete removal of such buffering connections impacts the entire molecular circuitry, ultimately leading to cellular death. Coexpression is commonly measured through Pearson
This often occurs through molecular buffering mechanisms that can be predicted using, among other features, the degree of coexpression between genes, commonly estimated through marginal measures of association such as Pearson or Spearman
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Pearson correlation values
marginal correlations
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correlation values
correlations
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on
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association within
coexpressed genes on
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or, in other words, of the represented edge within the entire network. Jones and West (2005) showed that covariance can be decomposed into the weights of the paths that connect two variables within the corresponding undirected network. Here we provide a precise interpretation of path weights and show that, in the particular case of single-edge paths, this interpretation leads to a quantity we call
, 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
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whose value depends on both the partial correlation between the intervening variables and their association with the rest of the multivariate system. We show that this new quantity correlates better with quantitative
and use it to analyze
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than classical coexpression measures