scaleFreeFitIndex_alternative

numeric vector whose components contain non-negative values

positive integer. This determines the number of equal width bins.

nBreaks

logical. If TRUE then the first bin will be removed.

removeFirst

(INTERNAL) This function is a copy of <code><a rd-options="WGCNA" href="/link/scaleFreeFitIndex?package=molnet&version=0.1.0&to=WGCNA" data-mini-rdoc="WGCNA::scaleFreeFitIndex">scaleFreeFitIndex</a></code>
with minor changes introduced to fit the needs of this package.
Description by <code><a rd-options="WGCNA" href="/link/scaleFreeFitIndex?package=molnet&version=0.1.0&to=WGCNA" data-mini-rdoc="WGCNA::scaleFreeFitIndex">scaleFreeFitIndex</a></code>: The function scaleFreeFitIndex
calculates several indices (fitting statistics) for evaluating scale free topology fit. The
input is a vector (of connectivities) k. Next k is discretized into nBreaks number of equal-
width bins. Let's denote the resulting vector dk. The relative frequency for each bin is
denoted p.dk.
The entire original function code is contained in the function and deleted lines are
commented out using <code>#--</code> to show changes.

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

scaleFreeFitIndex_alternative function

(INTERNAL) This function is a copy of <code><a rd-options='WGCNA' href='scaleFreeFitIndex'>scaleFreeFitIndex</a></code>
with minor changes introduced to fit the needs of this package.
Description by <code><a rd-options='WGCNA' href='scaleFreeFitIndex'>scaleFreeFitIndex</a></code>: The function scaleFreeFitIndex
calculates several indices (fitting statistics) for evaluating scale free topology fit. The
input is a vector (of connectivities) k. Next k is discretized into nBreaks number of equal-
width bins. Let's denote the resulting vector dk. The relative frequency for each bin is
denoted p.dk.
The entire original function code is contained in the function and deleted lines are
commented out using <code>#--</code> to show changes.

scaleFreeFitIndex_alternative: Alternative implementation of WGCNA::scaleFreeFitIndex

Description

Usage

Arguments

Value