sample_size

[INTERNAL] Depending on how missing data is handled in correlation matrix computation,
the number of samples used is returned. If `all.obs` is specified the number of rows (i.e. samples)
of the original data is returned. If `pairwise.complete.obs` is specified the crossproduct of a
matrix indicating the non-NA values is returned as matrix. This implementation was adopted
from <code>corAndPvalue</code>.

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

sample_size function

<dl><dt>measurement_data</dt>
<dd>[data.frame] Data frame containing the respective raw data (e.g. mRNA expression data,
protein abundance, etc.) to the adjacency matrix. Analyzed components (e.g. genes) in rows, samples (e.g. patients)
in columns.</dd>
<dt>handling_missing_data</dt>
<dd>["all.obs"|"pairwise.complete.obs"] Specifying the handling
of missing data during correlation matrix computation. (default: all.obs)</dd></dl>

Arguments

[INTERNAL] Sample size for correlation computation — sample_size

<dl>

<dt>measurement_data</dt>
<dd>[data.frame] Data frame containing the respective raw data (e.g. mRNA expression data,
protein abundance, etc.) to the adjacency matrix. Analyzed components (e.g. genes) in rows, samples (e.g. patients)
in columns.</dd>


<dt>handling_missing_data</dt>
<dd>["all.obs"|"pairwise.complete.obs"] Specifying the handling
of missing data during correlation matrix computation. (default: all.obs)</dd>

</dl>

sample_size: [INTERNAL] Sample size for correlation computation

Description

Usage

Value

Arguments