network_reduction_by_pickHardThreshold

[INTERNAL] This function uses <code>pickHardThreshold.fromSimilarity</code> to analyze
scale free topology for multiple hard thresholds. A cutoff is estimated, if no cutoff is
found the function terminates with an error message. All values below the cutoff will be set to NA and the
reduced adjacency is returned.

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

network_reduction_by_pickHardThreshold function

<dl><dt>adjacency_matrix</dt>
<dd>[matrix] Adjacency matrix of correlations computed using <code>cor</code> in
<code>compute_correlation_matrices</code></dd>
<dt>r_squared_cutoff</dt>
<dd>[float] A number indicating the desired minimum scale free topology fitting index R^2 for reduction
using <code>pickHardThreshold</code>. (default: 0.85)</dd>
<dt>cut_vector</dt>
<dd>[sequence of float] A vector of hard threshold cuts for which the scale free topology fit indices are to
be calculated during reduction with <code>pickHardThreshold</code>. (default: seq(0.2, 0.8, by = 0.01))</dd>
<dt>mean_number_edges</dt>
<dd>[int] Find a suitable edge weight cutoff employing <code>pickHardThreshold</code> to reduce
the network to at most the specified mean number of edges. Attention: This parameter overwrites the 'r_squared_cutoff' and
'edge_density' parameters if not set to NULL. (default: NULL)</dd>
<dt>edge_density</dt>
<dd>[float] Find a suitable edge weight cutoff employing <code>pickHardThreshold</code> to reduce the
network to at most the specified edge density. Attention: This parameter overwrites the 'r_squared_cutoff' parameter if not set
to NULL. (default: NULL)</dd></dl>

Arguments

[INTERNAL] Reduces network based on WGCNA::pickHardThreshold function — network_reduction_by_pickHardThreshold

<dl>

<dt>adjacency_matrix</dt>
<dd>[matrix] Adjacency matrix of correlations computed using <code>cor</code> in
<code>compute_correlation_matrices</code></dd>


<dt>r_squared_cutoff</dt>
<dd>[float] A number indicating the desired minimum scale free topology fitting index R^2 for reduction
using <code>pickHardThreshold</code>. (default: 0.85)</dd>


<dt>cut_vector</dt>
<dd>[sequence of float] A vector of hard threshold cuts for which the scale free topology fit indices are to
be calculated during reduction with <code>pickHardThreshold</code>. (default: seq(0.2, 0.8, by = 0.01))</dd>


<dt>mean_number_edges</dt>
<dd>[int] Find a suitable edge weight cutoff employing <code>pickHardThreshold</code> to reduce
the network to at most the specified mean number of edges. Attention: This parameter overwrites the 'r_squared_cutoff' and
'edge_density' parameters if not set to NULL. (default: NULL)</dd>


<dt>edge_density</dt>
<dd>[float] Find a suitable edge weight cutoff employing <code>pickHardThreshold</code> to reduce the
network to at most the specified edge density. Attention: This parameter overwrites the 'r_squared_cutoff' parameter if not set
to NULL. (default: NULL)</dd>

</dl>

network_reduction_by_pickHardThreshold: [INTERNAL] Reduces network based on WGCNA::pickHardThreshold function

Description

Usage

Value

Arguments