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24, chemin de Borde Rouge –Auzeville – CS52627
31326 Castanet Tolosan CEDEX - France

Dernière mise à jour : Mai 2018

Menu Logo Principal AgroParisTech Université Paris-Saclay


GABI : Génétique Animale et Biologie IntégrativeUnité Mixte de Recherche INRA - AgroParisTech

Estimation of causal effects using gene expression and intervention data

Within the framework of Gaussian Bayesian Networks, we have developed an MCMC procedure for the estimation of causal effects using trancriptomic and intervention data adapted to any design such as multiple or partial knock-outs or knock-downs.

Context and Stakes

Transcriptomic data are being used more and more for inference of gene networks. Until now, scientists have mainly used Gaussian Graphical Models which provide non directed graphs that are unable to illustrate the causal relations between genes. This work allows us to estimate causal effects between genes using a model combining transcriptomic data and intervention data obtained by knock-outs and knock-downs.


Within the framework of Gaussian Bayesian Networks, we have developed an MCMC procedure for the estimation of causal effects. The main advantage of our algorithm is that it can analyze any kind of design intervention as for example multiple or partial knock-outs, which are not part of the approaches that were proposed in the past.


We hope to apply this method to multiple knock-out data generated by Jean-Luc Vilotte's research team (MoDIT, GABI). We will then choose the optimal design for intervention experiments in order to validate inferred regulation networks and estimate causal effects.


Rau A, Jaffrézic F, Nuel G, 2013. Joint estimation of causal effects from observational and intervention gene expression data. BMC Systems Biology, 7: 111.