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) and Ha et. al., (2015) .

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.


iDINGO 1.0.2

  • Bug fix in plotNetwork.

iDINGO 1.0.1

  • Made minor edits to DESCRIPTION.

  • Added simple examples to manual files for idingo, dingo, and plotNetwork.

iDINGO 1.0.0

  • idingo performs integrative dingo for up to 3 platforms, sequentially connected.

  • Implemented parallel bootstrapping for idingo and dingo.

  • plotNetwork plots differential network from idingo or dingo.

Reference manual

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1.0.4 by Caleb A. Class, 13 days ago

Browse source code at

Authors: Caleb A. Class <[email protected]> , Min Jin Ha <[email protected]>

Documentation:   PDF Manual  

GPL-2 license

Imports mvtnorm, glasso, parallel, GGMridge, visNetwork, scales

Depends on igraph

See at CRAN