drug_targets_example

Exemplary intermediate pipeline output: Drug targets detected in the combined graphs. A named
list with elements `drug_targets` and `edgelist`. This was created from
<code><a rd-options="" href="/link/determine_drug_targets?package=molnet&version=0.1.0" data-mini-rdoc="molnet::determine_drug_targets">determine_drug_targets</a></code> using the default settings given by
<code><a rd-options="" href="/link/molnet_settings?package=molnet&version=0.1.0" data-mini-rdoc="molnet::molnet_settings">molnet_settings</a></code>. Drug-gene interactions to calculate this output were used from
The Drug Gene Interaction Database.

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

drug_targets_example function

A named list with 2 items.<dl class="dl-horizontal">
 <dt>targets</dt><dd>A named list</dd><dt>target_nodes</dt><dd>data frame with column `node_id` (unique node IDs in the graph targeted by
 drugs) and columns `group1` and `group2` (boolean values specifying whether the node is
 contained in the combined graph of the group)</dd>
 <dt>drugs_to_target_nodes</dt><dd>Element `drugs_to_target_nodes` contains a named list mapping drug
 names to a vector of their target node IDs.</dd><dt>edgelist</dt><dd>Contains elements `group1` and `group2` containing each a data frame of edges
 adjacent to drug target nodes each. Each edgelist data frame contains columns `from`, `to` and
 `weight`.</dd>
</dl>

Format

Exemplary intermediate pipeline output: Drug targets detected in the combined graphs. A named
list with elements `drug_targets` and `edgelist`. This was created from
<code><a rd-options='' href='determine_drug_targets'>determine_drug_targets</a></code> using the default settings given by
<code><a rd-options='' href='molnet_settings'>molnet_settings</a></code>. Drug-gene interactions to calculate this output were used from
The Drug Gene Interaction Database.

drug_targets_example: Drug target nodes in combined network

Description

Usage

Arguments

Format