Joint inference of tissue-specific networks with a scale free topology

High-throughput experimental techniques have produced an enormous number of gene expression profiles for various tissues of the human body. Tissue-specificity is a key component in reflecting the potentially different roles of proteins in diverse cell lineages. One way of understanding the tissue specificity is by reconstructing the tissue-specific co-expression networks (CENs) to analyze the correlation between genes. A few methods have been developed for estimating CENs, but it still remains challenging in terms of both accuracy and efficiency. In this paper we propose a new method, JointNet, for predicting tissue-specific co-expression networks. JointNet is exploiting the observation that, functionally related tissues have similar expression patterns and thus, similar networks. It uses different node penalties for hubs and non-hub nodes to accurately estimate the scale-free networks. Our experimental results show that the resulting tissue-specific CENs are accurate and that our method outperforms the current state of the art.