gtexture: Haralick texture analysis for graphs and its application to biological networks

Abstract

The calculation and use of Haralick texture features has been traditionally limited to imaging data and gray-level co-occurrence matrices calculated from images. We generalize the calculation of texture to graphs and networks with node attributes, focusing on cancer biology contexts such as fitness landscapes and gene regulatory networks with simulated and publicly available experimental gene expression data. We demonstrate the potential to calculate texture over multiple data set types including complex cancer networks and illustrate the potential for texture to distinguish cancer types and topologies of evolutionary landscapes through the summary metrics derived.