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BioMed Central, BMC Systems Biology, S6(9), 2015

DOI: 10.1186/1752-0509-9-s6-s6

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A model for gene deregulation detection using expression data

Journal article published in 2015 by Thomas Picchetti, Julien Chiquet, Mohamed Elati, Pierre Neuvial ORCID, Rémy Nicolle ORCID, Etienne Birmelé
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

In tumoral cells, gene regulation mechanisms are severely altered, and these modifications in the regulations may be characteristic of different subtypes of cancer. However, these alterations do not necessarily induce differential expressions between the subtypes. To answer this question, we propose a statistical methodology to identify the misregulated genes given a reference network and gene expression data. Our model is based on a regulatory process in which all genes are allowed to be deregulated. We derive an EM algorithm where the hidden variables correspond to the status (under/over/normally expressed) of the genes and where the E-step is solved thanks to a message passing algorithm. Our procedure provides posterior probabilities of deregulation in a given sample for each gene. We assess the performance of our method by numerical experiments on simulations and on a bladder cancer data set.