individual_graphs_example

Exemplary intermediate pipeline output: Individual graphs example data built by
<code><a rd-options="" href="/link/generate_individual_graphs?package=molnet&version=0.1.0" data-mini-rdoc="molnet::generate_individual_graphs">generate_individual_graphs</a></code>. Graphs were created by correlation computation and
reduced by the 'p_value' reduction method (default settings of <code><a rd-options="" href="/link/molnet_settings?package=molnet&version=0.1.0" data-mini-rdoc="molnet::molnet_settings">molnet_settings</a></code>. A
subset of the original data by Krug et al., 2020 and randomly sampled metabolite data
(<code><a rd-options="" href="/link/layers_example?package=molnet&version=0.1.0" data-mini-rdoc="molnet::layers_example">layers_example</a></code>) was used to generate graphs. They were created from data
tratified by estrogen receptor (ER) status: group1 contains data of ER+ patients and group2 of
ER- patients.

datasets

Networks provide a means to incorporate molecular interactions into
reasoning, but on the omics-level, they are currently mainly used to combine
genomic and proteomic information. We here present a novel network analysis
pipeline that enables integrative analysis of multi-omics data including
metabolomics. It allows for comparative conclusions between two different
conditions, such as tumor subgroups, healthy vs. disease, or generally control
vs. perturbed.
Our approach focuses on interactions and their strength instead of on node
properties and includes molecules with low abundance and unknown function. We
use correlation-induced networks that are reduced and combined to form
heterogeneous, multi-omics molecular networks. Prior information such as
metabolite-protein interactions are incorporated. A semi-local, path-based
integration step denoises the network and ensures integrative conclusions. As
case studies, we investigate differential drug response in breast cancer tumor
datasets providing proteomics, transcriptomics, phospho-proteomics and
metabolomics data and contrasting patients with different estrogen receptor
status.
Our proposed pipeline leverages multi-omics data for differential predictions,
e.g. on drug response, and includes prior information on interactions.
The case study presented in the vignette uses data published by
Krug (2020) <doi:10.1016/j.cell.2020.10.036>. The package license applies only
to the software and explicitly not to the included data.

Katharina Baum

molnet

Predicting Differential Drug Response using Multi-Omics Networks

Julian Hugo

Spoorthi Kashyap

Nataniel M<c3><bc>ller

Justus Zeinert

individual_graphs_example function

A named list with 2 items.<dl class="dl-horizontal">
 <dt>graphs</dt><dd>A named list with two groups.</dd><dt>group1</dt><dd>Graphs associated with group1</dd>
 <dl class="dl-horizontal">
 <dt>mrna</dt><dd></dd>
 <dt>protein</dt><dd></dd>
 <dt>phosphoprotein</dt><dd></dd>
 <dt>metabolite</dt><dd></dd>
 </dl>
 <dt>group2</dt><dd>same structure as above</dd><dt>annotations</dt><dd>A named list containing data frames of mappings of assigned node IDs to the
 user-provided component identifiers for nodes in group1 or group2 and all nodes</dd><dt>group1</dt><dd></dd>
 <dt>group2</dt><dd></dd>
 <dt>all</dt><dd></dd></dl>

Format

Exemplary intermediate pipeline output: Individual graphs example data built by
<code><a rd-options='' href='generate_individual_graphs'>generate_individual_graphs</a></code>. Graphs were created by correlation computation and
reduced by the 'p_value' reduction method (default settings of <code><a rd-options='' href='molnet_settings'>molnet_settings</a></code>. A
subset of the original data by Krug et al., 2020 and randomly sampled metabolite data
(<code><a rd-options='' href='layers_example'>layers_example</a></code>) was used to generate graphs. They were created from data
tratified by estrogen receptor (ER) status: group1 contains data of ER+ patients and group2 of
ER- patients.

individual_graphs_example: Individual graphs

Description

Usage

Arguments

Format