iDINGO——基于 Shiny 应用的基因组学综合差异网络分析。

iDINGO-integrative differential network analysis in genomics with Shiny application.

机构信息

Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

出版信息

Bioinformatics. 2018 Apr 1;34(7):1243-1245. doi: 10.1093/bioinformatics/btx750.

DOI:10.1093/bioinformatics/btx750

PMID:29194470

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6030922/

Abstract

MOTIVATION

Differential network analysis is an important way to understand network rewiring involved in disease progression and development. Building differential networks from multiple 'omics data provides insight into the holistic differences of the interactive system under different patient-specific groups. DINGO was developed to infer group-specific dependencies and build differential networks. However, DINGO and other existing tools are limited to analyze data arising from a single platform, and modeling each of the multiple 'omics data independently does not account for the hierarchical structure of the data.

RESULTS

We developed the iDINGO R package to estimate group-specific dependencies and make inferences on the integrative differential networks, considering the biological hierarchy among the platforms. A Shiny application has also been developed to facilitate easier analysis and visualization of results, including integrative differential networks and hub gene identification across platforms.

AVAILABILITY AND IMPLEMENTATION

R package is available on CRAN (https://cran.r-project.org/web/packages/iDINGO) and Shiny application at https://github.com/MinJinHa/iDINGO.

CONTACT

mjha@mdanderson.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

差异网络分析是理解疾病进展和发展中涉及的网络重连的重要方法。从多个“组学”数据构建差异网络，可以深入了解不同患者特定组中交互系统的整体差异。DINGO 是为推断特定于组的依赖性和构建差异网络而开发的。然而，DINGO 和其他现有工具仅限于分析来自单个平台的数据，并且独立地对多个“组学”数据进行建模并不能说明数据的层次结构。