% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/receptor.ppr.R
\docType{data}
\name{receptor.ppr}
\alias{receptor.ppr}
\title{Personalized PageRank Results for Signaling Network}
\format{
A list with three elements:
\describe{
  \item{\code{ppr}}{A numeric matrix representing the personalized PageRank results.
  Rows correspond to receptors (source nodes), and columns correspond to targets.
  Each entry reflects the regulatory potential of a receptor on a target gene.}
  \item{\code{ltf}}{A numeric value representing the quantile threshold learned
  via NicheNet integration. Entries in the \code{ppr} matrix with regulatory
  potential below this quantile are set to zero, effectively pruning low-confidence interactions.}
  \item{\code{tfs}}{A character vector of transcription factor (TF) gene names
  available in the \code{ppr} matrix. These TFs are potential targets of receptors in the regulatory network.}
}
}
\source{
Applying \code{igraph}'s \code{\link[igraph]{page_rank}} function on \code{\link{sig.graph}}.
}
\usage{
receptor.ppr
}
\description{
A list containing the results of running the personalized PageRank (PPR) algorithm
on \code{sig.graph}. The PPR algorithm evaluates the regulatory potential of
receptors on target genes within an intra-cellular signaling network. This list
provides a prior model and the necessary parameters for refining the PPR matrix.
}
\details{
The \code{ppr} matrix provides a prior model of receptor-target regulatory potential,
where the regulatory effect of each receptor is evaluated using personalized PageRank
with receptors as source nodes. The \code{ltf} threshold is used to prune low-confidence
interactions, ensuring only high-confidence regulatory potentials are retained.

This object is a key output of the NicheNet integration pipeline implemented by Ominipath, supporting downstream
analyses such as upstream signaling pathway (USP) inference and regulatory network refinement.
}
\examples{
# Access the PPR matrix
head(receptor.ppr$ppr)

# View the learned threshold
receptor.ppr$ltf

# Access the list of transcription factors
head(receptor.ppr$tfs)

# Prune the PPR matrix based on the learned threshold
pruned_ppr <- receptor.ppr$ppr
pruned_ppr[pruned_ppr < quantile(pruned_ppr, receptor.ppr$ltf)] <- 0

}
\seealso{
\code{\link{sig.graph}}, \code{\link{gr.graph}}
}
\keyword{datasets}
