The networked partial correlation and its application to the analysis of genetic interactions

TL;DR

This paper introduces the networked partial correlation, a new measure derived from covariance decomposition over gene networks, to better analyze genetic interactions and distinguish functional associations from genetic interactions in yeast.

Contribution

It proposes the networked partial correlation, addressing limitations of traditional correlation measures in analyzing gene interactions within networks.

Findings

Networked partial correlation effectively captures gene interactions.

It improves discrimination between functional associations and genetic interactions.

Application to yeast demonstrates its practical utility.

Abstract

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.