NetGreg (version 0.0.2)

identifyHubs: 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)

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).

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.

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.