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 (group1 = ER+, group2 = ER-). Each group is given as a sub-list
containing 'data' (raw data, metabolites in columns and samples in rows) and 'identifiers'
(one column per identifier, rows in the same order as the metabolites order in 'data').

datasets

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

metabolite_data function

<dl class="dl-horizontal">
 <dt>group1</dt><dd>ER+ data</dd><dt>data</dt><dd>raw data, metabolites in columns and samples in rows</dd>
 <dt>identifiers</dt><dd>one column per identifier, rows in the same order as the metabolite order
 in 'data', identifiers: biochemical name, METABOLON ID, Pubchem ID</dd><dt>group2</dt><dd>ER- data</dd><dt>data</dt><dd>see above</dd>
 <dt>identifiers</dt><dd>see above</dd></dl>

Format

Metabolomics data — metabolite_data

Metabolomics data

metabolite_data: Metabolomics data

Description

Usage

Arguments

Format