Graph "texture" features as novel metrics that can summarize complex biological graphs.

Objective 
Image texture features, such as those derived by Haralick et al., are a powerful metric for image classification and
are used across fields including cancer research. Our aim is to demonstrate how analogous texture features can be derived for graphs and networks. We also aim to illustrate how these new metrics summarize graphs, may aid comparative graph studies, may help classify biological graphs, and might assist in detecting dysregulation in cancer.

Approach 
We generate the first analogies of image texture for graphs and networks. Co-occurrence matrices for graphs are
generated by summing over all pairs of neighboring nodes in the graph. We generate metrics for fitness landscapes, gene
co-expression and regulatory networks, and protein interaction networks. To assess metric sensitivity we varied discretization
parameters and noise. To examine these metrics in the cancer context we compare metrics for both simulated and publicly
available experimental gene expression and build random forest classifiers for cancer cell lineage.

Main Results 
Our novel graph "texture" features are shown to be informative of graph structure and node label distributions.
The metrics are sensitive to discretization parameters and noise in node labels. We demonstrate that graph texture features
vary across different biological graph topologies and node labelings. We show how our texture metrics can be used to classify
cell line expression by lineage, demonstrating classifiers with 82% and 89% accuracy.

Significance 
New metrics provide opportunities for better comparative analyses and new models for classification. Our
texture features are novel second-order graph features for networks or graphs with ordered node labels. In the complex
cancer informatics setting, evolutionary analyses and drug response prediction are two examples where new network science
approaches like this may prove fruitful.