Multi-Omic Differentially Expressed Gene-Gene Pairs
Calculate the p values for specific category network samples
Compute interaction p values for a single percentile value
cat_parallel (from nestedcv)
Predict method for multiDEGGs_filter objects
Generate differential networks for single omic analysis
Generate multi-omic differential networks
Get a table of all significant interactions across categories
Get a table of all the significant interactions across categories
Combined multiDEGGs filter
multiDEGGs_filter
Internal function for colors
Boxplots of single nodes (genes,proteins, etc.)
Plot differential regressions for a link
Wrapper of .predict_multiDEGGs for multiDEGGs_filter_combined()
Wrapper of .predict_multiDEGGs for multiDEGGs_filter()
Tidying up of metadata. Samples belonging to undesidered categories (i...
Interactive visualisation of differential networks
Performs multi-omic differential network analysis by revealing differential interactions between molecular entities (genes, proteins, transcription factors, or other biomolecules) across the omic datasets provided. For each omic dataset, a differential network is constructed where links represent statistically significant differential interactions between entities. These networks are then integrated into a comprehensive visualization using distinct colors to distinguish interactions from different omic layers. This unified display allows interactive exploration of cross-omic patterns, such as differential interactions present at both transcript and protein levels. For each link, users can access differential statistical significance metrics (p values or adjusted p values, calculated via robust or traditional linear regression with interaction term) and differential regression plots. The methods implemented in this package are described in Sciacca et al. (2023) <doi:10.1093/bioinformatics/btad192>.
Useful links