#' netOmics: network-based multi-omics integration and interpretation

#'

#' netOmics is a multi-omics networks builder and explorer.

#' It uses a combination of network inference algorithms and and knowledge-based

#'  graphs to build multi-layered networks. 

#'  

#'  The package can be combined with 

#'  `timeOmics` to incorporate time-course expression data and build 

#'  sub-networks from multi-omics kinetic clusters.

#'  

#' Finally, from the generated multi-omics networks, propagation analyses allow

#'  the identification of missing biological functions (1), 

#' multi-omics mechanisms (2) and molecules between kinetic clusters (3). 

#' This helps to resolve complex regulatory mechanisms.

#' Here are the main functions.

#' 

#' @section Network building:

#' \describe{

#'   \item{`get_grn`}{Based on expression matrix, this function build a gene 

#'   gene regulatory network. Additionally, if clustering information is given,

#'   it builds cluster specific graph.}

#'   \item{`get_interaction_from_database`}{From a database (graph or data.frame 

#'   with interactions between 2 molecules), this function build the induced

#'   graph based on a list of molecules . Alternatively, the function can 

#'   build a graph with the first degree neighbors.}

#'   \item{`get_interaction_from_correlation`}{Compute correlation between two 

#'   dataframe X and Y (or list of data.frame).

#' An incidence graph is returned. A link between two features is produced 

#' if their correlation (absolute value) is above the threshold.}

#'   \item{`combine_layers`}{Combine 2 (or list of) graphs based on given 

#'   intersections.}

#'   }

#'

#' @section Network exploration:

#' \describe{

#'   \item{`random_walk_restart`}{This function performs a propagation analysis 

#'   by random walk with restart

#'  in a multi-layered network from specific seeds.}

#'   \item{`rwr_find_seeds_between_attributes`}{From rwr results, this function 

#'   returns a subgraph if any vertex shares 

#' different attributes value.

#' In biological context, this might be useful to identify vertex shared between

#'  clusters or omics types.}

#'   \item{`rwr_find_closest_type`}{From a rwr results, this function returns 

#'   the closest nodes from a seed with 

#' a given attribute and value.

#' In biological context, it might be useful to get the closest Gene Ontology

#'  annotation nodes from unannotated seeds.}

#'  }

#'  

#' @section Visualisation:

#' \describe{

#'   \item{`summary_plot_rwr_attributes`}{#' Based on the results of 

#' \code{\link[netOmics]{rwr_find_seeds_between_attributes}} which identify the

#'  closest k neighbors from a seed, this function returns a barplot of the node

#'   types (layers) reached for each seed.}

#'   \item{`plot_rwr_subnetwork`}{Display the subgraph from a RWR results. 

#'   This function colors adds a specific

#'  color to each node based on their 'type' attribute.

#' It also adds a legend including the number of vertices/edges and the number 

#' of nodes of specific type.

#' Additionally, the function can display any igraph object.}

#'  }

#' 

#'

#' @docType package

#' @name netOmics

#' 

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