An Augmented High-Dimensional Graphical Lasso Method to Incorporate Prior Biological Knowledge for Global Network Learning - Zhuang, Yonghua; Xing, Fuyong; Ghosh, Debashis; Banaei-Kashani, Farnoush; Bowler, Russell P.; Kechris, Katerina (Frontiers in Genetics, 2022)
Biological networks are often inferred through Gaussian graphical models (GGMs) using gene or protein expression data only. GGMs identify conditional dependence by estimating a precision matrix between genes or proteins. However, conventional GGM approaches often ignore prior knowledge about protein-protein interactions (PPI). Recently, several groups have extended GGM to weighted graphical lasso (wGlasso) and network-based gene set analysis (Netgsa) and have demonstrated the advantages of incorporating PPI information. However, these methods are either computationally intractable for large-scale data, or disregard weights in the PPI networks. To address these shortcomings, \textcolor{red}{we extended Netgsa approach and developed an augmented high-dimensional graphical lasso (AhGlasso)} method to incorporate edge weights in known protein-protein interactions with omics data for global network learning. This new method outperforms weighted graphical lasso-based algorithms with respect to computational time in simulated large-scale data settings while achieving better or comparable prediction accuracy of node connections. \textcolor{red}{The total runtime of AhGlasso is approximately five times faster than weighted Glasso methods when the graph size ranges from 1000 to 3000 with a fixed sample size (n = 300). The runtime difference between AhGlasso and weighted Glasso increases when the graph size increases.} Using proteomic data from a study on chronic obstructive pulmonary disease, we demonstrated that AhGlasso improves protein network inference compared to the Netgsa approach by incorporating PPI information.