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iDINGO-integrative differential network analysis in genomics with Shiny application.
Class, Caleb A; Ha, Min Jin; Baladandayuthapani, Veerabhadran; Do, Kim-Anh.
Afiliação
  • Class CA; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Ha MJ; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Baladandayuthapani V; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Do KA; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Bioinformatics ; 34(7): 1243-1245, 2018 04 01.
Article em En | MEDLINE | ID: mdl-29194470
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Progressão da Doença / Biologia Computacional Limite: Humans Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Progressão da Doença / Biologia Computacional Limite: Humans Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Estados Unidos