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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.

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Version

Install

install.packages('iDINGO')

Monthly Downloads

217

Version

1.0.4

License

GPL-2

Maintainer

Caleb Class

Last Published

July 30th, 2020

Functions in iDINGO (1.0.4)

plotNetwork

Plot differential network
scaledMat

scale a square matrix
brca

Modified TCGA Breast Cancer data
dingo

Fit DINGO model
single.boot

Calculating differential score for a single bootstrap
trans.Fisher

Fisher's Z-transformation
iDINGO-package

iDINGO: Integrative Differential Network Analysis in Genomics
Greg.em

Fitting precision regression models
extendedBIC

Extended bayesian information criteria for gaussian graphical models
Sigmax

group specific covariance matrices
gbm

Modified TCGA Glioblastoma data
idingo

Fit iDINGO model
scoring.boot.parallel

Calculating differential score with parallel bootstrap scoring
scoring.boot

Calculating differential score