--- a
+++ b/R/get_grn.R
@@ -0,0 +1,151 @@
+#' Gene Regulatory Network
+#' 
+#' Get Gene Regulatory Network (GRN) from a data.frame.
+#' Optionally, if the gene are clustered, sub_network are build for 
+#' each cluster.
+#' 
+#' @param X a \code{data.frame}/\code{matrix} with gene expression 
+#' (genes in columns, samples in rows).
+#' @param cluster (optional) clustering result from 
+#'      \code{\link[timeOmics]{getCluster}}
+#' @param method network building method, one of c('aracne')
+#' @param type character added to node metadata
+#' 
+#' 
+#' @details 
+#' Methods of GRN reconstruction are as follows:
+#' 'aracne': use ARACNe algorithm on Mutual Information (MI) adjency matrix 
+#' to remove low MI edges in triangles.
+#' 
+#' @return 
+#' An igraph object if no cluster informations are given. 
+#' Otherwise, it returns a list of igraph object (\code{list.igraph}) with 
+#' a subgraph for each cluster and a global graph with all the genes.
+#' 
+#' @seealso 
+#' \code{\link[minet]{build.mim}}, 
+#' \code{\link[minet]{aracne}}, 
+#' \code{\link[timeOmics]{getCluster}}
+#' 
+#' @examples
+#' data(hmp_T2D)
+#' # grn only on gene
+#' cluster.mRNA <- timeOmics::getCluster(hmp_T2D$getCluster.res, 
+#'                                       user.block = 'RNA')
+#' X <- hmp_T2D$raw$RNA
+#' grn.res <- get_grn(X = hmp_T2D$raw$RNA, 
+#'                    cluster = cluster.mRNA, 
+#'                    method = 'aracne')
+#' 
+#' 
+#' @importFrom minet build.mim aracne
+#' @importFrom magrittr %>%
+#' @importFrom dplyr select
+#' @importFrom purrr map
+#' @importFrom igraph set_vertex_attr graph_from_adjacency_matrix as.undirected
+#' @export
+get_grn <- function(X, 
+                    cluster = NULL, 
+                    method = c("aracne"),
+                    type = "gene"
+) {
+    
+    # check if X
+    X <- validate_matrix_X(X, var.name = "'X' ")
+    
+    # check cluster
+    cluster <- check_getCluster(cluster)
+    
+    # check method, for now only 1
+    method <- match.arg(method)
+    
+    # check type
+    type <- check_vector_char(type, X.length = 1, default = "gene")
+    
+    if (is.null(cluster)) {
+        # no clusteing info -> perform grn on all molecules
+        mim <- minet::build.mim(X)
+        grn.adj <- minet::aracne(mim)
+        grn.graph <- igraph::graph_from_adjacency_matrix(grn.adj) %>% 
+          igraph::as.undirected()
+        
+        # add type attribute 'type' <- 'Gene'
+        grn.graph <- igraph::set_vertex_attr(graph = grn.graph, 
+                                             name = "type", 
+                                             value = type)
+        grn.graph <- igraph::set_vertex_attr(graph = grn.graph, 
+                                             name = "mode", 
+                                             value = "core")
+        grn.graph <- igraph::set_vertex_attr(graph = grn.graph, 
+                                             name = "cluster", 
+                                             value = "All")
+        
+        # res <- list()
+        return(grn.graph)
+    } else {
+        # cluster != NULL we do have cluster info and data are clustered 1. grn
+        # for all
+        mim <- minet::build.mim(X)
+        grn.adj <- minet::aracne(mim)
+        grn.graph <- igraph::graph_from_adjacency_matrix(grn.adj) %>% 
+          igraph::as.undirected()
+                                                         
+        grn.graph <- igraph::set_vertex_attr(graph = grn.graph, 
+                                             name = "type", 
+                                             value = type)
+        grn.graph <- igraph::set_vertex_attr(graph = grn.graph, 
+                                             name = "mode", 
+                                             value = "core")
+        
+        res <- list()
+        res[["All"]] <- grn.graph
+        
+        # 2. grn by all clusters
+        mol_cluster <- cluster %>%
+            split(.$cluster) %>%
+            purrr::map(~.x$molecule)
+        X.by.cluster <- purrr::map(
+            mol_cluster, ~{
+                dplyr::select(
+                    as.data.frame(X, check.names = FALSE),
+                    .x
+                )
+            }
+        )
+        
+        # names_mol_cluster <- check_name_list(mol_cluster)
+        for (i in names(mol_cluster)) {
+            mim.cluster <- minet::build.mim(X.by.cluster[[i]])
+            grn.adj.cluster <- minet::aracne(mim.cluster)
+            grn.graph.cluster <- igraph::graph_from_adjacency_matrix(
+                grn.adj.cluster) %>% 
+              igraph::as.undirected()
+            grn.graph.cluster <- igraph::set_vertex_attr(
+                graph = grn.graph.cluster, 
+                name = "type", 
+                value = type)
+            grn.graph.cluster <- igraph::set_vertex_attr(
+                graph = grn.graph.cluster, 
+                name = "mode", 
+                value = "core")
+            grn.graph.cluster <- igraph::set_vertex_attr(
+                graph = grn.graph.cluster, 
+                name = "cluster", 
+                value = i)
+            
+            res[[i]] <- grn.graph.cluster
+            
+            # also add cluster info to 'All' graph
+            res[["All"]] <- igraph::set_vertex_attr(
+                graph = res[["All"]], name = "cluster", 
+                value = i, 
+                index = igraph::V(grn.graph.cluster)$name
+            )
+            
+        }
+        class(res) <- c("list.igraph")
+    }
+    return(res)
+}
+
+