% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/layers.R
\name{compute_tf_network}
\alias{compute_tf_network}
\title{Compute TF network and add it to hummus object}
\usage{
compute_tf_network(
  hummus,
  organism = 9606,
  tfs = NA,
  gene_assay = NULL,
  method = NULL,
  store_network = FALSE,
  output_file = NULL,
  source_target = "AND",
  multiplex_name = "TF",
  tf_network_name = "TF_network",
  verbose = 1
)
}
\arguments{
\item{hummus}{(Hummus_Object) - Hummus object}

\item{organism}{(integer)  - Specie identifier from Omnipath to fetch
specific interactions}

\item{tfs}{vector(character) - List of tfs consider. If NA, tfs are extracted
from the hummus object with get_tfs function.}

\item{gene_assay}{(character) - Name of the assay to get tfs from if tfs is
not provided. If NULL, all TFs with motifs in the hummus object are used.}

\item{method}{(character) - Method used to infer network edges.
\itemize{
\item \code{'Omnipath'} - Use Omnipath to infer tf-tf networks.
\item \code{'NULL'} - A fake connected network is computed.
\item \code{'Other method'} - TO DO.
}}

\item{store_network}{(bool) - Save the network directly (\code{TRUE},
default) or return without saving on disk (\code{FALSE}).}

\item{output_file}{(character) - Name of the output_file
(if store_network == \code{TRUE}).}

\item{source_target}{('AND'|'OR') - Fetch only the interactions involving
two considered tfs (\code{'AND', default}), or one considered tfs and any
other element (\code{'OR'})}

\item{multiplex_name}{(character) - Name of the multiplex to add the network
to. Default is \code{'TF'}.}

\item{tf_network_name}{(character) - Name of the network in the multiplex to
add the network to. Default is \code{'TF_network'}.}

\item{verbose}{(integer) - Display function messages. Set to 0 for no message
displayed, >= 1 for more details.}
}
\value{
(Hummus_Object) - Return hummus object with the new network added.
}
\description{
Compute a protein-protein interaction layer from Omnipath request that will represent tf cooperativity.
This network is the top-layer of HuMMuS multilayer.
}
\examples{
hummus <- compute_tf_network(hummus,
                                       gene_assay = "RNA",
                                       verbose = 1)
}