Gene clustering via integrated Markov models combining individual and pairwise features.
Clustering of genes into groups sharing common characteristics is a useful exploratory technique for a number of subsequent computational analysis. A wide range of clustering algorithms have been proposed in particular to analyze gene expression data, but most of them consider genes as independent entities or include relevant information on gene interactions in a suboptimal way. We propose a probabilistic model that has the advantage to account for individual data (e.g., expression) and pairwise data (e.g., interaction information coming from biological networks) simultaneously. Our model is based on hidden Markov random field models in which parametric probability distributions account for the distribution of individual data. Data on pairs, possibly reflecting distance or similarity measures between genes, are then included through a graph, where the nodes represent the genes, and the edges are weighted according to the available interaction information. As a probabilistic model, this model has many interesting theoretical features. In addition, preliminary experiments on simulated and real data show promising results and points out the gain in using such an approach.


BACKGROUND
The software used in this work is written in C++ and is available with other supplementary material at http://mistis.inrialpes.fr/people/forbes/transparentia/supplementary.html.
Vignes
Matthieu
M
BioSS at the Scottish Crop Research Institute, Invergowrie, Dundee, Scotland, UK. matthieu@bioss.ac.uk
Forbes
Florence
F
D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006019:Glycolysis; D008390:Markov Chains; D053858:Metabolic Networks and Pathways; D005810:Multigene Family; D012319:RNA Polymerase II / Q000378:metabolism; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D012984:Software
D012319:RNA Polymerase II