The Joint Censored Gaussian Graphical Lasso Model

Abstract

The Gaussian graphical model is one of the most used tools for inferring genetic networks. Nowadays, the data are often collected from different sources or under different biological conditions, resulting in heterogeneous datasets that exhibit a dependency structure that varies across groups. The complex structure of these data is typically recovered using regularized inferential procedures that use two penalties, one that encourages sparsity within each graph and the other that encourages common structures among the different groups. To this date, these approaches have not been developed for handling the case of censored data. However, these data are often generated by gene expression technologies such as RT-qPCR experiments. In this paper, we fill this gap and propose an extension of joint Gaussian graphical modelling to account for censored, or more generally missing, data.