Quantitatively predicting phenotype variables by the expression changes in a set of candidate genes is of great interest in molecular biology but it is also a challenging task for several reasons. First, the collected biological observations might be heterogeneous and correspond to different biological mechanisms. Secondly, the gene expression variables used to predict the phenotype are potentially highly correlated since genes interact through unknown regulatory networks. In this talk, we present a novel approach designed to predict quantitative traits from transcriptomic data, taking into account the heterogeneity in biological observations and the hidden gene regulatory networks. The proposed model performs well on prediction but it is also fully model-based, which facilitates the downstream biological interpretation. The model provides clusters of individuals based on the relation between gene expression data and the phenotype, and also leads to infer a gene regulatory network specific for each cluster of individuals.
We perform numerical simulations to demonstrate that our model is competitive with other prediction models, and we demonstrate the predictive performance and the interpretability of our model to predict olfactory behavior from transcriptomic data on real data from Drosophila Melanogaster Genetic Reference Panel (DGRP).