individual_graphs_example: Individual graphs

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

Individual graphs

Description

Exemplary intermediate pipeline output: Individual graphs example data built by generate_individual_graphs. Graphs were created from correlation_matrices_example and reduced by the 'pickHardThreshold' reduction method. Used settings were:

Usage

individual_graphs_example

Format

A named list with 2 items.

graphs

A named list with two groups.

groupA

Graphs associated with 'groupA'

mrna

Graph

protein

Graph

phosphosite

Graph

metabolite

Graph

groupB

same structure as 'groupA'

annotations

A named list containing data frames of mappings of assigned node IDs to the user-provided component identifiers for nodes in 'groupA' or 'groupB' and all nodes

groupA

Annotations associated with 'groupA'

mrna

Data frame

protein

Data frame

phosphosite

Data frame

metabolite

Data frame

groupB

same structure as 'groupA'

both

same structure as 'groupA'

Details

settings <- drdimont_settings( reduction_method=list(default="pickHardThreshold"), r_squared=list( default=0.8, groupA=list(metabolite=0.45), groupB=list(metabolite=0.15)), cut_vector=list( default=seq(0.3, 0.7, 0.01), metabolite=seq(0.1, 0.65, 0.01)))

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 and individual 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.