B2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, CIBER-BBN, Barcelona 08028, Spain.
Department of Biomedical Engineering, Institut de Recerca Pediàtrica Hospital Sant Joan de Déu, Esplugues de Llobregat, Barcelona 08950, Spain.
Bioinformatics. 2018 Feb 1;34(3):533-534. doi: 10.1093/bioinformatics/btx632.
Label propagation and diffusion over biological networks are a common mathematical formalism in computational biology for giving context to molecular entities and prioritizing novel candidates in the area of study. There are several choices in conceiving the diffusion process-involving the graph kernel, the score definitions and the presence of a posterior statistical normalization-which have an impact on the results. This manuscript describes diffuStats, an R package that provides a collection of graph kernels and diffusion scores, as well as a parallel permutation analysis for the normalized scores, that eases the computation of the scores and their benchmarking for an optimal choice.
The R package diffuStats is publicly available in Bioconductor, https://bioconductor.org, under the GPL-3 license.
Supplementary data are available at Bioinformatics online.
在计算生物学中，基于生物网络的标签传播和扩散是一种常见的数学形式，可用于为分子实体赋予上下文并确定研究领域中新型候选物的优先级。在构想扩散过程时，有多种选择——包括图核、评分定义和后验统计归一化的存在——这些选择会对结果产生影响。本文描述了 diffuStats，这是一个 R 包，它提供了一系列图核和扩散评分，以及标准化评分的并行置换分析，这使得评分的计算和最佳选择的基准测试变得更加容易。
R 包 diffuStats 可在 Bioconductor，https://bioconductor.org 上公开获得，遵循 GPL-3 许可证。
补充数据可在 Bioinformatics 在线获得。
