Kernel integration by Graphical LASSO

Integration of unstructured and very diverse data is often required for a deeper understanding of complex biological systems. In order to uncover communalities between heterogeneous data, the data is often harmonized by constructing a kernel and numerical integration is performed. In this study we propose a method for data integration in the framework of an undirected graphical model, where the nodes represent individual data sources of varying nature in terms of complexity and underlying distribution, and where the edges represent the partial correlation between two blocks of data. We propose a modified GLASSO for estimation of the graph, with a combination of cross-validation and extended Bayes Information Criterion for sparsity tuning. Furthermore, hierarchical clustering on the weighted consensus kernels from a fixed network is used to partitioning the samples into different classes. Simulations show increasing ability to uncover true edges with increasing sample size and signal to noise. Likewise, identification of non existing edges towards disconnected nodes is feasible. The framework is demonstrated for integration of longitudinal symptom burden data from the 2nd and 3rd year of life with 21 diseases precursors as well as the development of asthma and eczema at the age of 6 years from 403 children from the COPSAC2010 mother-child cohort, suggesting that maternal predisposition as well as being born preterm indirectly lead to higher risk of asthma via increased respiratory symptom burden.