README.md

In sbarkerclarke-phd/CoOccurR: Generalized Application of Co-Occurrence Matrices and Haralick Texture

gtexture: R Package for the calculation of Haralick texture features for graphs and networks

Overview

Gray-level co-occurrence matrices (GLCM) have been applied with success in fields such as medical imaging. However, GLCMs could provide valuable insight when applied to objects other than images. We believe that the study of fitness landscapes, within the field of evolutionary biology, could benefit from analysis via GLCMs. To that end, this package generalizes the methodology underlying GLCMs to non-imaging data. The current focus is, as previously mentioned, on application of GLCMs to fitness landscapes.

Installation

The gtexture R package can be installed as follows:

# install from CRAN (coming soon)
# install.packages("gtexture")

# install using the remotes package
remotes::install_github("rbarkerclarke/gtexture")

Data Availability

Data used for the Physics in Medicine and Biology paper (https://iopscience.iop.org/article/10.1088/1361-6560/ace305) was sourced from the Cancer Cell Line Encyclopedia. Mahmoud Ghandi, Franklin W. Huang, Judit Jané-Valbuena, Gregory V. Kryukov, ... Todd R. Golub, Levi A. Garraway & William R. Sellers. 2019. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).

Features

Coming soon...

sbarkerclarke-phd/CoOccurR documentation built on April 5, 2024, 1:48 p.m.