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+#' 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
+#' 
+NULL
+#> NULL
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