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)
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).
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
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
idingo, based on a user-specified p-value or differential score threshold.
Made minor edits to
Added simple examples to manual files for
idingo performs integrative
dingo for up to 3 platforms, sequentially connected.
Implemented parallel bootstrapping for
plotNetwork plots differential network from