metabolite_data

Metabolomics analysis of breast cancer patients data sampled randomly to
generate distributions similar to those reported (e.g., in Terunuma et al. (2014)).
The data is stratified by estrogen receptor (ER) expression status (`groupA` = ER+, `groupB` = ER-). 
The data was reduced to 50 metabolites. 
For each group a data frame is given containing the raw data with the metabolites as rows and the
samples as columns. The first three columns contain the metabolite identifiers
(biochemical_name, metabolon_id and pubchem_id).

datasets

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

metabolite_data function

<dl>
 <dt>groupA</dt>
<dd>ER+ data; data.frame: first three columns contain metabolite identifiers
 biochemical_name, metabolon_id and pubchem_id;
 other columns are samples containing the quantified metabolite data per metabolite</dd> <dt>groupB</dt>
<dd>ER- data; data.frame: first three columns contain metabolite identifiers
 biochemical_name, metabolon_id and pubchem_id;
 other columns are samples containing the quantified metabolite data per metabolite</dd>
</dl>

Format

Metabolomics data — metabolite_data

<dl>
 <dt>groupA</dt>
<dd>ER+ data; data.frame: first three columns contain metabolite identifiers
 biochemical_name, metabolon_id and pubchem_id;
 other columns are samples containing the quantified metabolite data per metabolite</dd>

 <dt>groupB</dt>
<dd>ER- data; data.frame: first three columns contain metabolite identifiers
 biochemical_name, metabolon_id and pubchem_id;
 other columns are samples containing the quantified metabolite data per metabolite</dd>


</dl>

metabolite_data: Metabolomics data

Description

Usage

Arguments

Format