A model for gene deregulation detection using expression data.

Rémy Nicolle,Mohamed Elati,Thomas Picchetti,Julien Chiquet,Etienne Birmelé,Pierre Neuvial

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

Abstract

In tumoral cells, gene regulation mechanisms are severely altered. Genes that do not react normally to their regulators' activity can provide explanations for the tumoral behavior, and be characteristic of cancer subtypes. We thus 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.

Highlights

Various mechanisms affect gene expression in tumoral cells, including copy number alterations, mutations, modifications in the regulation network between the genes
We match our result with Copy Number Alteration (CNA) data collected from the same samples, in order to support that our method correctly identifies deregulated gene-sample pairs
Given a reference gene regulatory network (GRN), it allows to determine which genes are misregulated in a sample, meaning an expression which does not match the network given the expression of its regulators

Summary

Introduction

Various mechanisms affect gene expression in tumoral cells, including copy number alterations, mutations, modifications in the regulation network between the genes. Results can be extended to the scale of pathways using enrichment analysis [1] or functional class scoring [2] Such a strategy is blind to small variations in gene expression, especially as multiple testing correction applies. It does not take interdependence between genes into account and can mark an expression change as abnormal when it is induced by a change in the regulators’ activity. To overcome these drawbacks, an alternative strategy is to identify the affected genes by pointing important changes in the gene regulatory network (GRN) of the tumoral cell. Such an approach corresponds to the modelisation of phenomena altering regulation, as for instance mutations in regulatory regions [3]

Objectives

Methods

Results

Conclusion