# diffuStats: compute diffusion scores over networks [](https://travis-ci.org/b2slab/diffuStats) [](https://codecov.io/github/b2slab/diffuStats?branch=master) ## Introduction The general purpose `diffuStats` R package offers a collection of seven network propagation scores and five graph kernels. Those find application in ubiquitous computational biology applications, being one representative example the propagation of genetic information (e.g. disease-associated genes) in a gene-gene or a protein-protein interaction network. A distinctive feature of `diffuStats` is the implementation of statistically normalised scores, which address the recurrent question of how would the propagation of a randomised input look. It offers parametric, exact z-scores as well as permutation-based empirical probabilities. The `diffuStats` software was published in: > Picart-Armada, S., Thompson, W. K., Buil, A., & Perera-Lluna, A. (2018). diffuStats: an R package to compute diffusion-based scores on biological networks. Bioinformatics, 34(3), 533-534. General guidelines on how to choose the scores, along with mathematical properties of the normalised and unnormalised scores, were published in: > Picart-Armada, S., Thompson, W. K., Buil, A., & Perera-Lluna, A. (2020). The effect of statistical normalisation on network propagation scores. Bioinformatics, btaa896. From versions 1.10.2/1.11.2 onwards, `diffuStats` provides functions to export the exact statistical moments (means and variances), see `?moments`. Now the users can characterise the systematic biases in the diffusion scores in their domain of application. ## Installation `diffuStats` is part of Bioconductor, and can be installed using ```{r} BiocManager::install("diffuStats") ``` For the development version, you can also install the package through `R CMD INSTALL` or through `devtools::install_github("b2slab/diffuStats")`, which points to its [GitHub repository](https://github.com/b2slab/diffuStats). ## Getting started `diffuStats` is suitable for medium-sized networks (thousands of nodes) and is conceived to be used in biological networks. Its limitations come from the kernel formalism: networks exceeding 20k nodes will start requiring large kernel matrices in memory. Get started by looking at the package vignettes (`intro` for a quickstart, `diffuStats` for a complete documentation) or its help ``` ?diffuStats ``` ## News File `NEWS.md` keeps track of the additions and bug fixes of each package version.