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Type: Package
Title: Network-Guided Penalized Regression (NetGreg)
Version: 0.0.2
Description: A network-guided penalized regression framework that integrates network characteristics from Gaussian graphical models with partial penalization, accounting for both network structure (hubs and non-hubs) and clinical covariates in high-dimensional omics data, including transcriptomics and proteomics. The full methodological details can be found in our recent preprint by Ahn S and Oh EJ (2025) <doi:10.48550/arXiv.2505.22986>.
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.3.2
Depends: R (≥ 3.5.0)
Imports: huge, glmnet, dplyr, stats, plsgenomics
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
NeedsCompilation: no
Packaged: 2025-05-30 04:40:17 UTC; seungjunahn
Author: Seungjun Ahn ORCID iD [cre, aut], Eun Jeong Oh ORCID iD [aut]
Maintainer: Seungjun Ahn <seungjun.ahn@mountsinai.org>
Repository: CRAN
Date/Publication: 2025-06-03 09:30:12 UTC

NetworkGuided

Description

A main function to obtain network-guided penalized regression coefficient estimates.

Usage

NetworkGuided(Y, X, hubs, Z, nfolds = 5)

Arguments

Y

A continuous outcome variable.

X

A data matrix of dimension n x p representing samples (rows) by features (columns).

hubs

A vector of hubs idenfitied through identifyHubs function from our package.

Z

A matrix of clinical or demographic covariates.

nfolds

A user-specified numeric value for k-fold cross-validation.

Value

A vector of network-guided penalized regression coefficients.

Examples

library(plsgenomics)
data(Colon) ## Data from plsgenomics R package
X = data.frame(Colon$X[,1:100]) ## The first 100 genes
Z = data.frame(Colon$X[,101:102]) ## Two clinical covariates
colnames(Z) = c("Z1", "Z2")
Y = as.vector(Colon$X[,1000])  ## Continuous outcome variable

## Apply identifyHubs():
preNG = identifyHubs(X=X, delta=0.05, tau=5, ebic.gamma = 0.1)

## Explore preNG results:
hubs = preNG$hubs ## Returns the names of the identified hub nodes.

## Use our main NetworkGuided function, to obtain network-guided
## penalized regression coefficient estimates.
NG = NetworkGuided(Y=Y, X=X, hubs=preNG$hubs, Z=Z, nfolds=5)
NG$coef

identifyHubs

Description

A function to identify hub nodes (i.e., genes or proteins) from high-dimensional data using network-based criteria.

Usage

identifyHubs(X, delta, tau, ebic.gamma = 0.1)

Arguments

X

A data matrix of dimension n x p representing samples (rows) by features (columns).

delta

A numeric value indicating the proportion of nodes to considered as hubs in a network.

tau

A user-specified cutoff for the number of hubs.

ebic.gamma

A numeric value specifying the tuning parameter for the extended Bayesian information criterion (eBIC) used in network estimation.

Value

A list containing (1) the selected sparse graph structure and model selection results; (2) a data frame of feature names with their associated network characteristics (e.g., degree centrality); and (3) a character vector of top-ranked hub features (e.g., hub genes or proteins).

Examples

library(plsgenomics)
data(Colon) ## Data from plsgenomics R package
X = data.frame(Colon$X[,1:100]) ## The first 100 genes
Z = data.frame(Colon$X[,101:102]) ## Two clinical covariates
colnames(Z) = c("Z1", "Z2")
Y = as.vector(Colon$X[,1000])  ## Continuous outcome variable

## Apply identifyHubs():
preNG = identifyHubs(X=X, delta=0.05, tau=5, ebic.gamma = 0.1)

## Explore preNG results:
## To display the degree centrality for each node,
## sorted from strongest to weakest.
preNG$assoResults
preNG$hubs ## Returns the names of the identified hub nodes.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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