Abstract
Complex diseases are believed to be the consequence of intracellular network(s) involving a range of factors. An improved understanding of a disease-predisposing biological network could lead to better identification of genes and pathways that confer disease risk and therefore inform drug development. The group difference in biological networks, as is often characterized by graphs of nodes and edges, is attributable to effects of these nodes and edges. Here we introduced pointwise mutual information (PMI) as a measure of the connection between a pair of nodes with either a linear relationship or nonlinear dependence. We then proposed a PMI-based network regression (PMINR) model to differentiate patterns of network changes (in node or edge) linking a disease outcome. Through simulation studies with various sample sizes and inter-node correlation structures, we showed that PMINR can accurately identify these changes with higher power than current methods and be robust to the network topology. Finally, we illustrated, with publicly available data on lung cancer and gene methylation data on aging and Alzheimer’s disease, an evaluation of the practical performance of PMINR. We concluded that PMI is able to capture the generic inter-node correlation pattern in biological networks, and PMINR is a powerful and efficient approach for biological network analysis.
Highlights
A complex disease is understood to be the consequence not of abnormality involving a single biomolecule (e.g., RNA, protein, metabolite) but of their network(s) and possibly a variety of other factors (Barabási et al, 2011)
A greater understanding of the role of biological network(s) in disease etiology and treatment should lead to better identification of disease-related genes and pathways, and to more precise targets for drug development
In an attempt to accommodate changes in nodes and edges which lead to network differences, we previously developed statistics to test the group difference for weighted biological networks (Ji et al, 2016), for pathways with chain structure (Ji et al, 2015; Yuan et al, 2016a) and for directed biological networks (Yuan et al, 2016b)
Summary
A complex disease is understood to be the consequence not of abnormality involving a single biomolecule (e.g., RNA, protein, metabolite) but of their network(s) and possibly a variety of other factors (Barabási et al, 2011). Biomolecules interact with each other in such network(s) which underpin the disease pathogenesis and progression. A recent gene set analysis method has emphasized the importance of incorporating network or pathway information (Li et al, 2019). There is a framework of “think globally, act locally” in great need to develop statistical methods to detect whether specific biological network is strongly associated with the disease outcome. A greater understanding of the role of biological network(s) in disease etiology and treatment should lead to better identification of disease-related genes and pathways, and to more precise targets for drug development
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