create_unique_layer_node_ids

[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 <code>generate_differential_score_graph</code> to calculate
differential scores of identical nodes in both sample groups.
The function is used by the high-level wrapper generate_individual_graphs to
create annotations, which uniquely define nodes across the network layer.

internal

While it has been well established that drugs affect and help
patients differently, personalized drug response predictions remain
challenging. Solutions based on single omics measurements have been proposed,
and networks provide means to incorporate molecular interactions into reasoning.
However, how to integrate the wealth of information contained in multiple omics
layers still poses a complex problem.
We present a novel network analysis pipeline, DrDimont, Drug response prediction
from Differential analysis of multi-omics networks. It allows for comparative
conclusions between two conditions and translates them into differential drug
response predictions. DrDimont focuses on molecular interactions. It establishes
condition-specific networks from correlation within an omics layer that are
then reduced and combined into heterogeneous, multi-omics molecular networks.
A novel semi-local, path-based integration step ensures integrative conclusions.
Differential predictions are derived from comparing the condition-specific
integrated networks. DrDimont's predictions are explainable, i.e., molecular
differences that are the source of high differential drug scores can be retrieved.
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

DrDimont

Drug Response Prediction from Differential Multi-Omics Networks

Pauline Hiort

Julian Hugo

Spoorthi Kashyap

Nataniel Müller

Justus Zeinert

create_unique_layer_node_ids function

<dl><dt>identifiersA, identifiersB</dt>
<dd>[data.frame] Containing the biological identifiers of each
group of the same network layer.</dd>
<dt>layer_name</dt>
<dd>[string] Name of layer that the node ids are created for</dd></dl>

Arguments

[INTERNAL] Assign node IDs to the biological identifiers across a graph layer — create_unique_layer_node_ids

<dl>

<dt>identifiersA, identifiersB</dt>
<dd>[data.frame] Containing the biological identifiers of each
group of the same network layer.</dd>


<dt>layer_name</dt>
<dd>[string] Name of layer that the node ids are created for</dd>

</dl>

create_unique_layer_node_ids: [INTERNAL] Assign node IDs to the biological identifiers across a graph layer

Description

Usage

Value

Arguments