#' 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)

}