#' Get interaction from ORA enrichment analysis

#'

#' Returns results of an ORA analysis as an interaction graph

#'

#' @param query a vector (or a list) of character with the ID to perform

#'      the ORA analysis

#' @param sources (optional) a character in

#'    (GO, KEGG, REAC, TF, MIRNA, CORUM, HP, HPA, WP)

#' @param organism (optional) a character (default = 'hsapiens')

#' @param signif.value (optional) a logical, default = ''

#'

#' @return

#' a graph object (or list of graph) containing the interaction between

#' the query and the target terms.

#'

#' @seealso \code{\link[gprofiler2]{gost}}  \code{\link[gprofiler2]{gconvert}}

#'

#' @examples

#' query <- c('IL15', 'CDHR5', 'TGFA', 'C4B')

#' get_interaction_from_ORA(query,

#'                          sources = 'GO')

#'

#' query <- list('All' = c('IL15', 'CDHR5', 'TGFA', 'C4B'),

#'               'c1' = c('IL15', 'CDHR5', 'TGFA'))

#' get_interaction_from_ORA(query,

#'                          sources = 'GO')

#'

#' @importFrom gprofiler2 gost gconvert

#' @importFrom dplyr pull select filter

#' @export

get_interaction_from_ORA <- function(query,

                                     sources = "GO",

                                     organism = "hsapiens",

                                     signif.value = TRUE) {

  # validate query (char)

  if (is(query, "list")) {

    query <- lapply(query,

                    function(x)

                      check_vector_char(x,

                                        var.name = "'query '"))

    if (is.null(names(query))) {

      names(query) <- seq_along(query)

    }

  } else {

    query <- check_vector_char(query)

  }

  # check organism

  organism = check_vector_char(

    X = organism,

    X.length = 1,

    default = "hsapiens",

    var.name = "'organism' "

  )

  # check source

  sources <- match.arg(

    arg = sources,

    choices = c("GO", "KEGG", "REAC", "TF", "MIRNA", "CORUM", "HP",

                "HPA", "WP"),

    several.ok = FALSE

  )

  sources <-

    check_vector_char(sources, default = "GO")  # default value

  # check signif

  signif.value <- return_true_false(signif.value, default = TRUE)

  if (is(query, "list")) {

    res.ora <- list()

    term_map <- list()

    res.graph <- list()

    for (i in names(query)) {

      res.ora[[i]] <- get_ORA(query = query[[i]],

                              sources = sources,

                              organism = organism)

      term_map_tmp <- gprofiler2::gconvert(query = query[[i]],

                                           organism = organism,

                                           target = sources)

      target_id <- (

        res.ora[[i]] %>%

          dplyr::filter(significant == signif.value) %>%

          dplyr::pull(term_id)

      )

      term_map[[i]] <- term_map_tmp %>%

        dplyr::filter(target %in% target_id) %>%

        dplyr::select(input, target) %>%

        unique %>%

        na.omit()

      res.graph[[i]] <-

        igraph::graph_from_data_frame(term_map[[i]],

                                      directed = FALSE)

      res.graph[[i]] <- set_vertex_attr(

        graph = res.graph[[i]],

        name = "mode",

        index = term_map[[i]]$input,

        value = "core"

      )

      res.graph[[i]] <- set_vertex_attr(

        graph = res.graph[[i]],

        name = "mode",

        index = term_map[[i]]$target,

        value = "extended"

      )

      class(res.graph) <-

        c("list.interaction.igraph", "list.igraph")

    }

  } else {

    # query is not a list

    res.ora <- get_ORA(query = query,

                       sources = sources,

                       organism = organism)

    term_map_tmp <- gprofiler2::gconvert(query = query,

                                         organism = organism,

                                         target = sources)

    target_id <- (res.ora %>%

                    dplyr::filter(significant == signif.value) %>%

                    dplyr::pull(term_id))

    if (is.null(term_map_tmp)) {

      return(NULL)

    }

    term_map <- term_map_tmp %>%

      dplyr::filter(target %in% target_id) %>%

      dplyr::select(input, target) %>%

      unique %>%

      na.omit()

    res.graph <-

      igraph::graph_from_data_frame(term_map, directed = FALSE)

    res.graph <- set_vertex_attr(

      graph = res.graph,

      name = "mode",

      index = term_map$input,

      value = "core"

    )

    res.graph <- set_vertex_attr(

      graph = res.graph,

      name = "mode",

      index = term_map$target,

      value = "extended"

    )

    class(res.graph) <- c("interaction.igraph", "igraph")

  }

  return(res.graph)

}

#' ORA enrichment analysis

#'

#' Returns results of an ORA analysis

#'

#' @param query a vector of character, a lit of ID

#' @param sources a character or list of character

#' @param organism a character (default = 'hsapiens')

#'

#' @return

#' a data.frame containing the enrichment result

#'

#' @seealso \code{\link[gprofiler2]{gost}}

#'

#' @importFrom gprofiler2 gost

get_ORA <- function(query,

                    sources = NULL,

                    organism = "hsapiens") {

  if (is(query, "list")) {

    res <- list()

    for (i in names(query)) {

      ORA <- gprofiler2::gost(

        query = query[[i]],

        organism = organism,

        significant = FALSE,

        sources = sources,

        multi_query = FALSE

      )

      ORA.res <- ORA$result

      if (!is.null(ORA.res)) {

        ORA.res <- ORA.res %>%

          mutate(cluster = i) %>%

          dplyr::select(

            "cluster",

            "term_id",

            "source",

            "term_name",

            "p_value",

            "significant",

            "term_size",

            "query_size",

            "intersection_size",

            "precision",

            "recall"

          )

        res[[i]] <- ORA.res

      }

    }

    RES <- purrr::map_dfr(res, ~ .x)

  } else {

    ORA <- gprofiler2::gost(

      query = query,

      organism = organism,

      significant = FALSE,

      sources = sources,

      multi_query = FALSE

    )

    ORA.res <- ORA$result

    if (!is.null(ORA.res)) {

      ORA.res <- ORA.res %>%

        mutate(cluster = "All") %>%

        dplyr::select(

          "cluster",

          "term_id",

          "source",

          "term_name",

          "p_value",

          "significant",

          "term_size",

          "query_size",

          "intersection_size",

          "precision",

          "recall"

        )

      RES <- ORA.res

    } else {

      RES <- NULL

    }

  }

  return(RES)

}

#' Get GO info

#'

#' From a GO terms (GOID), return definition, ontology and term values

#'  from GO.db

#'

#' @param go a character, GO term

#'

#' @return

#' a data.frame with the following columns: 'GOID', 'DEFINITION',

#' 'ONTOLOGY', 'TERM'

#'

#' @import GO.db

#' @importFrom AnnotationDbi keytypes

get_go_info <- function(go) {

  res <- AnnotationDbi::select(

    x = GO.db,

    keys = go,

    keytype = "GOID",

    columns = keytypes(GO.db)

  )

  return(res)

}