differential_score_graph_example

<p>Exemplary intermediate pipeline output: Differential score graph example data built by
<code><a rd-options="" href="/link/differential_score?package=molnet&version=0.1.0" data-mini-rdoc="molnet::differential_score">differential_score</a></code>. Contains one graph carrying the differential interaction score
as weight. This was computed using a subset of the data published 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>.</p>

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

differential_score_graph_example function

Format

<p>Exemplary intermediate pipeline output: Differential score graph example data built by
<code><a rd-options='' href='differential_score'>differential_score</a></code>. Contains one graph carrying the differential interaction score
as weight. This was computed using a subset of the data published by Krug et al., 2020 and
randomly sampled metabolite data <code><a rd-options='' href='layers_example'>layers_example</a></code>.</p>

Differential graph — differential_score_graph_example

Differential graph

differential_score_graph_example: Differential graph

Description

Usage

Arguments

Format