Mixture models for analysing transcriptome and ChIP-chip data – Marie Laure Martin Magniette


Mixture models are useful for identifying underlying structures. In such models, the density of the observations is modelled by a weighted sum of parametric density (e.g. each component is a Gaussian distribution) and each one represents a subpopulation composed of observations sharing common characteristics. The first part of my talk will be dedicated to a presentation of the mixture models. I will explain the concept and the outputs of an analysis based on a mixture through easy examples. In the second part of my talk, I will show how mixture models can be applied to analyze transcriptomic (co‐expression analysis of Arabidopsis thaliana genes) and chIP‐chip data (detection of enriched regions and of differentially methylated regions).

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