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multiDEGGs

Differentially Expressed Gene-Gene pairs in multi omic data

The multiDEGGs package test for differential gene-gene correlations across different groups of samples in multi omic data.
Specific gene-gene interactions can be explored and gene-gene pair regression plots can be interactively shown.

Installation

Install from CRAN:
install.packages("multiDEGGs")

Install from Github:
devtools::install_github("elisabettasciacca/multiDEGGs")

Example

Load package and sample data
library(multiDEGGs) data("synthetic_metadata") data("synthetic_rnaseqData") data("synthetic_proteomicData") data("synthetic_OlinkData")

Generate differential networks
`assayData_list <- list(“RNAseq” = synthetic_rnaseqData, “Proteomics” = synthetic_proteomicData, “Olink” = synthetic_OlinkData)

deggs_object <- get_diffNetworks(assayData = assayData_list, metadata = synthetic_metadata, category_variable = “response”, regression_method = “lm”, padj_method = “bonferroni”, verbose = FALSE, show_progressBar = FALSE, cores = 2)`

Visualise interactively (will open a shiny interface)
View_diffNetworks(deggs_object)

Get a table listing all the significant interactions found in each category
get_multiOmics_diffNetworks(deggs_object, sig_threshold = 0.05)

Plot differential regression fits for a single interaction
plot_regressions(deggs_object, assayDataName = "RNAseq", gene_A = "MTOR", gene_B = "AKT2", legend_position = "bottomright")

Citation

citation("multiDEGGs")

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.