combined_graphs_example: Combined graphs

In DrDimont: Drug Response Prediction from Differential Multi-Omics Networks

Combined graphs

Description

Exemplary intermediate pipeline output: Combined graphs example data built by generate_combined_graphs. Combined graphs were built using the individual_graphs_example and:

Usage

combined_graphs_example

Format

A named list with 2 items.

graphs

A named list with two groups.

groupA

Graph associated with 'groupA'

groupB

Graph associated with 'groupB'

annotations

A data frame of mappings of assigned node IDs to the user-provided component identifiers for all nodes in 'groupA' and 'groupB' together and all layers

both

Data frame

Details

inter_layer_connections = list( make_connection(from='mrna', to='protein', connect_on='gene_name', weight=1), make_connection(from='protein', to='phosphosite', connect_on='gene_name', weight=1), make_connection(from='protein', to='metabolite', connect_on=metabolite_protein_interactions, weight='combined_score'))

A subset of the original data by Krug et al. (2020) and randomly sampled metabolite data from layers_example was used to generate the correlation matrices, individual graphs and combined graphs. They were created from data stratified by estrogen receptor (ER) status: 'groupA' contains data of ER+ patients and 'groupB' of ER- patients.

Source

Krug, Karsten et al. “Proteogenomic Landscape of Breast Cancer Tumorigenesis and Targeted Therapy.” Cell vol. 183,5 (2020): 1436-1456.e31. doi:10.1016/j.cell.2020.10.036

DrDimont documentation built on Sept. 23, 2022, 5:06 p.m.