Package: iDINGO 1.0.4

Caleb A. Class

iDINGO: Integrative Differential Network Analysis in Genomics

Fits covariate dependent partial correlation matrices for integrative models to identify differential networks between two groups. The methods are described in Class et. al., (2018) <doi:10.1093/bioinformatics/btx750> and Ha et. al., (2015) <doi:10.1093/bioinformatics/btv406>.

Authors:Caleb A. Class <cclass@butler.edu>, Min Jin Ha <mjha@mdanderson.org>

iDINGO_1.0.4.tar.gz
iDINGO_1.0.4.tar.gz(r-4.5-noble)iDINGO_1.0.4.tar.gz(r-4.4-noble)
iDINGO_1.0.4.tgz(r-4.4-emscripten)iDINGO_1.0.4.tgz(r-4.3-emscripten)
iDINGO.pdf |iDINGO.html✨
iDINGO/json (API)
NEWS

# Install 'iDINGO' in R:
install.packages('iDINGO', repos = 'https://cloud.r-project.org')
Datasets:
  • brca - Modified TCGA Breast Cancer data
  • gbm - Modified TCGA Glioblastoma data

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

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Last updated 5 years agofrom:f080579709. Checks:3 OK. Indexed: yes.

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Exports:dingoextendedBICGreg.emidingoplotNetworkscaledMatscoring.bootscoring.boot.parallelSigmaxsingle.boottrans.Fisher

Dependencies:base64encbslibcachemclicolorspacecpp11digestevaluatefarverfastmapfontawesomefsGGMridgeglassogluehighrhtmltoolshtmlwidgetsigraphjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemimemunsellmvtnormpkgconfigR6rappdirsRColorBrewerrlangrmarkdownsassscalestinytexvctrsviridisLitevisNetworkxfunyaml

Citation

To cite iDINGO in publications, please use:

Caleb A Class, Min Jin Ha, Veerabhadran Baladandayuthapani, Kim-Anh Do. iDINGO-integrative differential network analysis in genomics with Shiny application. Bioinformatics, Volume 34, Issue 7, 01 April 2018, Pages 1243-1245.

Min Jin Ha, Veerabhadran Baladandayuthapani, Kim-Anh Do. DINGO: differential network analysis in genomics. Bioinformatics, Volume 31, Issue 21, 1 November 2015, Pages 3413-3420.

Corresponding BibTeX entries: 

  @Article{,
    author = {Caleb A Class and Min Jin Ha and Veerabhadran
      Baladandayuthapani and Kim-Anh Do},
    title = {iDINGO-integrative differential network analysis in
      genomics with Shiny application},
    journal = {Bioinformatics},
    volume = {34},
    number = {7},
    pages = {1243-1245},
    year = {2017},
    month = {11},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btx750},
  }

  @Article{,
    author = {Min Jin Ha and Veerabhadran Baladandayuthapani and
      Kim-Anh Do},
    title = {DINGO: differential network analysis in genomics},
    journal = {Bioinformatics},
    volume = {31},
    number = {21},
    pages = {3413-3420},
    year = {2015},
    month = {07},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btv406},
  }

Readme and manuals

iDINGO: Integrative Differential Network Analysis in Genomics

iDINGO is a pathway-based method for estimating group-specific conditional dependencies and inferring differential networks between groups, based on genomic data. This can be done in a single-platform framework (for example, RNA-Seq data) or an integrative multi-platform framework (microRNA -> RNA -> Proteomics, where data from all three platforms are available for every sample).

Using iDINGO

We recommend filtering genomic data to fewer than 300 genes, generally filtered using a pathway/pathways of interest. Single-platform analyses are run using dingo with an nxp matrix, where n is the number of samples. Multi-platform analyses are run using idingo, with up to 3 separate data matrices containing the same n samples. For both dingo and idingo, the number of bootstraps is specified by B (we recommend at least 100). Parallel computing can speed this step up significantly, by setting the number of cores. Finally, the plotNetwork function plots the differential network identified by dingo or idingo, based on a user-specified p-value or differential score threshold.