create_unique_layer_node_ids

Data frames containing the biological identifiers of each
group of the same network layer.

identifiers1, identifiers2

Name of layer node ids are created for

layer_name

(INTERNAL) This function takes two data frames of (biological) identifiers of nodes. Each data
frame corresponds to the identifiers of the components contained in the single-layer network of
 a sample group.
This function outputs the same data frames, with an added column (`node_id`) that contains node
 IDs which can later be used as `name` parameter for an iGraph graph.
Node IDs begin with the defined `prefix` and an underscore. If a molecule is present in both
groups, the node ID will be the same across the whole layer, allowing to easily combine the
graphs of both groups in `differential_score()` to calculate differential scores of identical
nodes in both sample groups.
The function is used by the high-level wrapper <a rd-options="molnet" href="/link/generate_individual_graphs?package=molnet&version=0.1.0&to=molnet" data-mini-rdoc="molnet::generate_individual_graphs">generate_individual_graphs</a> to
create annotations, which uniquely define nodes across the network layer.

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

create_unique_layer_node_ids function

(INTERNAL) This function takes two data frames of (biological) identifiers of nodes. Each data
frame corresponds to the identifiers of the components contained in the single-layer network of
 a sample group.
This function outputs the same data frames, with an added column (`node_id`) that contains node
 IDs which can later be used as `name` parameter for an iGraph graph.
Node IDs begin with the defined `prefix` and an underscore. If a molecule is present in both
groups, the node ID will be the same across the whole layer, allowing to easily combine the
graphs of both groups in `differential_score()` to calculate differential scores of identical
nodes in both sample groups.
The function is used by the high-level wrapper <a rd-options='molnet' href='generate_individual_graphs'>generate_individual_graphs</a> to
create annotations, which uniquely define nodes across the network layer.

create_unique_layer_node_ids: Assigns node IDs to the biological identifiers across a graph layer

Description

Usage

Arguments

Value