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SUMMARY:The Networked Partial Correlation and its Application to the Analy
 sis of Genetic Interactions - Alberto Roverato (Università di Bologna)
DTSTART:20160826T080000Z
DTEND:20160826T084000Z
UID:TALK67066@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Genetic interactions confer robustness on cells in response to
  genetic perturbations. This often occurs through molecular buffering mech
 anisms 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. Howev
 er\, marginal correlations are sensitive to indirect effects and often par
 tial correlations are used instead. Yet\, partial correlations convey no i
 nformation about the (linear) influence of the coexpressed genes on the en
 tire 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 deco
 mposition over the paths of the associated gene network. We call this new 
 quantity the networked partial correlation and use it to analyze genetic i
 nteractions in yeast. More concretely\, in its well-characterized leucine 
 biosynthesis pathway and on a previously published data set of genome-wide
  quantitative genetic interaction profiles. In both cases\, networked part
 ial correlations substantially improve the identification of genetic inter
 actions over classical coexpression measures.
LOCATION:Seminar Room 1\, Newton Institute
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