| individual_graphs_example {DrDimont} | R Documentation |
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