PhenoExamWeb: Compare the likeness of phenotypes terms between groups of genes

Documented in CGIloadder CGIloaddercompare CGIloaddercompareOpt CGIloadderOpt CLINGENloadder CLINGENloaddercompare CLINGENloaddercompareOpt CLINGENloadderOpt convertgenesymbolmessage CRBloadder CRBloaddercompare CRBloaddercompareOpt CRBloadderOpt CTDloadder CTDloaddercompare CTDloaddercompareOpt CTDloadderOpt entrezmap findoverlapgenes FindPhenoFromGenes foundgeneoverlap GENOMICS_ENGLANDloadder GENOMICS_ENGLANDloaddercompare GENOMICS_ENGLANDloaddercompareOpt GENOMICS_ENGLANDloadderOpt getdbnames HPOloadder HPOloaddercompare HPOloaddercompareOpt HPOloadderOpt ListGenesPheno MGDloadder MGDloaddercompare MGDloaddercompareOpt MGDloadderOpt ORPHANETloadder ORPHANETloaddercompare ORPHANETloaddercompareOpt ORPHANETloadderOpt PhenoEnrichCompare PhenoEnrichCompareRandom PhenoEnrichGenes PhenoEnrichGenespran PhenoEnrichGenespransimu PhenoEnrichRandomOpt PhenoGeneNumber PhenoGeneNumberOpt PhenoGeneNumberSimuok PhenoRelation PSYGENETloadder PSYGENETloaddercompare PSYGENETloaddercompareOpt PSYGENETloadderOpt QueryGenes RandomComparePheno UNIPROTloadder UNIPROTloaddercompare UNIPROTloaddercompareOpt UNIPROTloadderOpt

#' @title QueryGenes
#' @description It produces a data frame with the phenotypical terms from the genes selected and their entrez ids.
#' @param genes A vector with the genes symbol
#' @param database A string paramater with different options. You can define a vector with the databases.
#' @param organism A string paramater: "human" if you have human genes symbol and "mouse" if you have mouse genes symbol
#' @details This function has developed by Alejandro Cisterna García as part of his PhD mentored by Juan Antonio Botía Blaya
#' @import readr
#' @import data.table
#' @import ggplot2
#' @import stats
#' @import plotly
#' @import ggpubr
#' @import dplyr
#' @import viridis
#' @import clusterProfiler
#' @import parallel
#' @import purrr
#' @import DT
#' @import Hmisc
#' @import pheatmap
#' @export
#'

QueryGenes = function(genes, database = "HPO", organism = "human"){

  if (organism == "human") {

    if (database == "UNIPROT") {
      # Read human data base
      hpox <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                           "/UNIPROTbasededatos.csv"))

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Symbol to entrez
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his HP
      onlyg <- inner_join(hpox, genes, by = "entrez")
    }

    if (database == "CGI") {
      # Read human data base
      hpox <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                           "/CGIbasededatos.csv"))

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Symbol to entrez
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his HP
      onlyg <- inner_join(hpox, genes, by = "entrez")
    }

    if (database == "PSYGENET") {
      # Read human data base
      hpox <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                           "/PSYGENETbasededatos.csv"))

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Symbol to entrez
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his HP
      onlyg <- inner_join(hpox, genes, by = "entrez")
    }

    if (database == "ORPHANET") {
      # Read human data base
      hpox <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                           "/ORPHANETbasededatos.csv"))

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Symbol to entrez
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his HP
      onlyg <- inner_join(hpox, genes, by = "entrez")
    }

    if (database == "CTD") {
      # Read human data base
      hpox <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                           "/CTD_humanbasededatos.csv"))

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Symbol to entrez
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his HP
      onlyg <- inner_join(hpox, genes, by = "entrez")
    }

    if (database == "GENOMICS_ENGLAND") {
      # Read human data base
      hpox <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                           "/GENOMICS_ENGLANDbasededatos.csv"))

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Symbol to entrez
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his HP
      onlyg <- inner_join(hpox, genes, by = "entrez")
    }

    if (database == "CLINGEN") {
      # Read human data base
      hpox <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                           "/clingenbasededatos.csv"))

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his HP
      onlyg <- inner_join(hpox, genes, by = "entrez")
    }

    if (database == "DIS") {
      # Read human data base
      hpox <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                           "/disbasededatos.csv"))

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his HP
      onlyg <- inner_join(hpox, genes, by = "entrez")
    }

    if (database == "HPO") {
      # Read human data base
      hpox <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                           "/hpobasededatos.csv"))

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his HP
      onlyg <- inner_join(hpox, genes, by = "entrez")
    }

    if (database == "CRB") {
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/cbrdatabaseok.csv"), header = T)

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his HP
      onlyg <- inner_join(custom, genes, by = "entrez")
    }

    if (database == "MGD"  | database == "MOUSEDB") {
      # Read mouse data base
      musedata <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                               "/mgibasefinal.tsv"))

      names(musedata) <- c("entrez","symbol", "mouse_symbol", "mgi", "term_id", "term_name", "info", "source"   )

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his MP
      onlyg <- inner_join(musedata, genes, by = "entrez")
      onlyg <- onlyg[,c(1,2,5,6)]
    }


  }


  if (organism == "mouse") {

    # genes = musedata[1:50,3]

    genes <- as.data.frame(genes)
    names(genes) <- "mouse_symbol"
    genes$mouse_symbol <- as.character(genes$mouse_symbol)

    # Read database
    homologia <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                              "/homology.csv"), header = T)

    # We will keep only the selected genes
    onlygenes <- dplyr::inner_join(genes, homologia, by = "mouse_symbol")
    onlygenes <- onlygenes$human_symbol
    onlygenes <- as.data.frame(onlygenes)
    names(onlygenes) <- "symbol"
    onlygenes$symbol <- as.character(onlygenes$symbol)
    genes = unique(onlygenes)

    if (database == "UNIPROT") {
      # Read human data base
      hpox <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                           "/UNIPROTbasededatos.csv"))

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Symbol to entrez
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his HP
      onlyg <- inner_join(hpox, genes, by = "entrez")
    }

    if (database == "CGI") {
      # Read human data base
      hpox <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                           "/CGIbasededatos.csv"))

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Symbol to entrez
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his HP
      onlyg <- inner_join(hpox, genes, by = "entrez")
    }

    if (database == "PSYGENET") {
      # Read human data base
      hpox <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                           "/PSYGENETbasededatos.csv"))

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Symbol to entrez
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his HP
      onlyg <- inner_join(hpox, genes, by = "entrez")
    }

    if (database == "ORPHANET") {
      # Read human data base
      hpox <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                           "/ORPHANETbasededatos.csv"))

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Symbol to entrez
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his HP
      onlyg <- inner_join(hpox, genes, by = "entrez")
    }

    if (database == "CTD") {
      # Read human data base
      hpox <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                           "/CTD_humanbasededatos.csv"))

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Symbol to entrez
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his HP
      onlyg <- inner_join(hpox, genes, by = "entrez")
    }

    if (database == "GENOMICS_ENGLAND") {
      # Read human data base
      hpox <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                           "/GENOMICS_ENGLANDbasededatos.csv"))

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Symbol to entrez
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his HP
      onlyg <- inner_join(hpox, genes, by = "entrez")
    }

    if (database == "CLINGEN") {
      # Read human data base
      hpox <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                           "/clingenbasededatos.csv"))

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his HP
      onlyg <- inner_join(hpox, genes, by = "entrez")
    }

    if (database == "DIS") {
      # Read human data base
      hpox <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                           "/disbasededatos.csv"))

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his HP
      onlyg <- inner_join(hpox, genes, by = "entrez")
    }

    if (database == "HPO") {
      # Read human data base
      hpox <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                           "/hpobasededatos.csv"))

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his HP
      onlyg <- inner_join(hpox, genes, by = "entrez")
    }

    if (database == "CRB") {
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/cbrdatabaseok.csv"), header = T)

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his HP
      onlyg <- inner_join(custom, genes, by = "entrez")
    }

    if (database == "MGD"  | database == "MOUSEDB") {
      # Read mouse data base
      musedata <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                               "/mgibasefinal.tsv"))

      names(musedata) <- c("entrez","symbol", "mouse_symbol", "mgi", "term_id", "term_name", "info", "source"   )

      # Convert vector to a dataframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # We will keep only with the genes in the query and his MP
      onlyg <- inner_join(musedata, genes, by = "entrez")
      onlyg <- onlyg[,c(1,2,5,6)]
    }

  }


  return(onlyg)
}


#' @title getdbnames
#' @description This function show the databases available in PhenoExamWeb package.
#' @details This function has developed by Alejandro Cisterna García as part of his PhD mentored by Juan Antonio Botía Blaya
#' @export
getdbnames = function(){

  sources <- c("HPO", "CRB", "MGD", "CLINGEN",
               "GENOMICS_ENGLAND", "CTD", "ORPHANET",
               "PSYGENET", "CGI", "UNIPROT")
  return(sources)
}


#' @title findoverlapgenes
#' @description This function get the genes that are linked to the phenotypes selected.
#' @param phenos A vector with the phenotypes id that you want.
#' @details This function has developed by Alejandro Cisterna García as part of his PhD mentored by Juan Antonio Botía Blaya
#' @export

findoverlapgenes = function(phenos){

  # Un archivo base de datos con todas las DB
  sdb <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                      "/sdb.csv"), header = T)


  # Make an empty df with names
  rbjointri = data.frame(matrix(vector(), 0, 5,
                                dimnames=list(c(), c("entrez", "symbol",   "term_name", "term_id", "source"))),
                         stringsAsFactors=F)


  aparece <- length(phenos)
  # For each database name in a vector run its loadder
  for (ph in phenos) {

    fenos = dplyr::filter(sdb, sdb$term_id == ph)


    # join data frames generated
    rbjointri <- rbind(rbjointri, fenos)



  }

  genesaparecen <-   rbjointri %>% dplyr::group_by(symbol,entrez) %>% dplyr::tally()  %>%
    dplyr::filter(n >= aparece)

  genesoverlap <- unique(genesaparecen$symbol)

  return(genesoverlap)
}

#' @title ListGenesPheno
#' @description This function make a list with genes selected and his phenotypical terms
#' @param genes A vector with the genes symbol
#' @param database A string paramater with different options. You can define a vector with the databases.
#' @param organism A string paramater: "human" if you have human genes symbol and "mouse" if you have mouse genes symbol
#' @details This function has developed by Alejandro Cisterna García as part of his PhD mentored by Juan Antonio Botía Blaya
#' @export

ListGenesPheno = function(genes, database = "HPO", organism = "human"){


  # We will keep only with the genes in the query and his HP
  onlyg <- QueryGenes(genes, database, organism)


    # We make a list with genes and his HP
    gbygen <-  split(onlyg$term_id , onlyg$symbol)



  return(gbygen)
}



#' @title FindPhenoFromGenes
#' @description Get the genes selected and only their phenotypical terms defined by the user.
#' @param genes A vector with genes symbol.
#' @param phenoid A vector with id terms to get.
#' @param database A string paramater with different options:  "HPO", "DIS", "CRB" and "MGD".
#' @param organism A string paramater: "human" if you have human genes symbol and "mouse" if you have mouse genes symbol
#' @details This function has developed by Alejandro Cisterna García as part of his PhD mentored by Juan Antonio Botía Blaya
#' @export


FindPhenoFromGenes = function(genes, phenoid, database = "HPO", organism = "human"){

  # We will keep only with the genes in the query and his phenotypic terms
  onlyg <- QueryGenes(genes,database, organism )

  # Convert vector to a dataframe
  phenoid <- as.data.frame(phenoid)
  names(phenoid) <- "term_id"

  # We only keep genes and particular phenotypic term
  onlygenesandpehno <- inner_join(onlyg, phenoid, by = "term_id")

  return(onlygenesandpehno)
}



#' @title PhenoGeneNumber
#' @description This function counts the number of genes per phenotype term from the vector of genes.
#' @param database A string paramater with different options. You can define a vector with the databases.
#' @param genes A vector with human gene symbol.
#' @param organism A string paramater: "human" if you have human genes and "mouse" if you have mouse genes
#' @details This function has developed by Alejandro Cisterna García as part of his PhD mentored by Juan Antonio Botía Blaya
#' @export


PhenoGeneNumber = function(genes, database= "HPO", organism = "human"){

  if (organism == "human") {


    if (database == "UNIPROT") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/UNIPROTbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "CGI") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/CGIbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "PSYGENET") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/PSYGENETbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "ORPHANET") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/ORPHANETbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "CTD") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/CTD_humanbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "GENOMICS_ENGLAND") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/GENOMICS_ENGLANDbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "CLINGEN") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/clingenbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "DIS") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/disbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "HPO") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/hpobasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "CRB") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/cbrdatabaseok.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")
    }

    if (database == "MGD") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "human_symbol"
      genes$HumanSymbol <- as.character(genes$human_symbol)

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$human_symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/mgibasefinal.tsv"), header = T, sep = "\t")
      names(custom) <- c("entrez","human_symbol", "mouse_symbol", "mgi", "term_id", "term_name", "info" , "source"  )

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name ) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")

    }
  }

  if (organism == "mouse") {

    genes <- as.data.frame(genes)
    names(genes) <- "mouse_symbol"
    genes$mouse_symbol <- as.character(genes$mouse_symbol)

    # Read database
    homologia <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                              "/homology.csv"), header = T)

    # We will keep only the selected genes
    onlygenes <- dplyr::inner_join(genes, homologia, by = "mouse_symbol")
    onlygenes <- onlygenes$human_symbol
    onlygenes <- as.data.frame(onlygenes)
    names(onlygenes) <- "symbol"
    onlygenes$symbol <- as.character(onlygenes$symbol)
    genes = unique(onlygenes)

    if (database == "UNIPROT") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/UNIPROTbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "CGI") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/CGIbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "PSYGENET") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/PSYGENETbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "ORPHANET") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/ORPHANETbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "CTD") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/CTD_humanbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "GENOMICS_ENGLAND") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/GENOMICS_ENGLANDbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "CLINGEN") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/clingenbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "DIS") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/disbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "HPO") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/hpobasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "CRB") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/cbrdatabaseok.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")
    }

    if (database == "MGD") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "human_symbol"
      genes$HumanSymbol <- as.character(genes$human_symbol)

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$human_symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/mgibasefinal.tsv"), header = T, sep = "\t")
      names(custom) <- c("entrez","human_symbol", "mouse_symbol", "mgi", "term_id", "term_name", "info" , "source"  )

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name ) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")

    }

  }

  return(numportergenes)
}



#' @title PhenoGeneNumberOpt
#' @description This function counts the number of genes per phenotype term from the vector of genes.
#' @param database A string paramater with different options. You can define a vector with the databases.
#' @param genes A vector with human gene symbol.
#' @param organism A string paramater: "human" if you have human genes and "mouse" if you have mouse genes
#' @details This function has developed by Alejandro Cisterna García as part of his PhD mentored by Juan Antonio Botía Blaya
#' @export


PhenoGeneNumberOpt = function(genes, database= "HPO", organism = "human"){

  if (organism == "human") {



    if (database == "UNIPROT") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/UNIPROTbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "CGI") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))


      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/CGIbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "PSYGENET") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/PSYGENETbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "ORPHANET") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))


      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/ORPHANETbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "CTD") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))


      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/CTD_humanbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "GENOMICS_ENGLAND") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/GENOMICS_ENGLANDbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "CLINGEN") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))


      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/clingenbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "DIS") {

      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/disbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "HPO") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/hpobasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")

    }


    if (database == "CRB") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))


      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/cbrdatabaseok.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")
    }

    if (database == "MGD") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/mgibasefinal.tsv"), header = T, sep = "\t")
      names(custom) <- c("entrez","human_symbol", "mouse_symbol", "mgi", "term_id", "term_name", "info" , "source"  )

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name ) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")

    }
  }

  if (organism == "mouse") {

    # genes = musedata[1:50,3]

    genes <- as.data.frame(genes)
    names(genes) <- "mouse_symbol"
    genes$mouse_symbol <- as.character(genes$mouse_symbol)

    # Read database
    homologia <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                              "/homology.csv"), header = T)

    # We will keep only the selected genes
    onlygenes <- dplyr::inner_join(genes, homologia, by = "mouse_symbol")
    onlygenes <- onlygenes$human_symbol
    onlygenes <- as.data.frame(onlygenes)
    names(onlygenes) <- "symbol"
    onlygenes$symbol <- as.character(onlygenes$symbol)
    genes = unique(onlygenes)


    if (database == "UNIPROT") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/UNIPROTbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "CGI") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/CGIbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "PSYGENET") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/PSYGENETbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "ORPHANET") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/ORPHANETbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "CTD") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/CTD_humanbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "GENOMICS_ENGLAND") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/GENOMICS_ENGLANDbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "CLINGEN") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/clingenbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "DIS") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/disbasededatos.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "HPO") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/hpobasededatos.csv"), header = T)
      names(custom) <- c("entrez","symbol", "term_id", "term_name")

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")
    }

    if (database == "CRB") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/cbrdatabaseok.csv"), header = T)
      names(custom) <- c("entrez","symbol", "term_name", "term_id")

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")
    }

    if (database == "MGD") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "human_symbol"
      genes$HumanSymbol <- as.character(genes$human_symbol)

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$human_symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/mgibasefinal.tsv"), header = T, sep = "\t")
      names(custom) <- c("entrez","human_symbol", "mouse_symbol", "mgi", "term_id", "term_name", "info"  , "source" )

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name ) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")

    }
  }

  return(numportergenes)
}


#' @title RandomComparePheno
#' @description This function measures statistically significant phenotype similarities between gene sets and detects significant differential phenotypes for them.
#' @param geneset A vector with genes symbol.
#' @param genesetcompare A vector with gene symbol.
#' @param organism A string paramater: "human" if you have human genes and "mouse" if you have mouse genes
#' @param database A string paramater with different options. You can define a vector with the databases.
#' @param p.value Cut of p value as they are relevant terms
#' @param nulltestnumber A number of random genes subset.
#' @param seed A seed.
#' @param url A logical paramater. T if you want interactive links
#' @details This function has developed by Alejandro Cisterna García as part of his PhD mentored by Juan Antonio Botía Blaya
#' @export


RandomComparePheno = function(geneset, genesetcompare,  organism = "human", database = "HPO", p.value = 0.05, nulltestnumber = 100, seed = 1, conditional_case = F, url = F){

  if (conditional_case == F) {

    # Get the Phenotype Enrichment Analysis from geneset
    referencia = PhenoEnrichGenes(genes=geneset, database, organism, url = url)
    enrichresult = as.data.frame(referencia$htmltabla)

    # Obtain the significative phenotypes terms from geneset
    relevantes <- dplyr::filter(enrichresult, enrichresult$adjust_pvalue <= p.value)


    # Get the Phenotype Enrichment Analysis from genesetcompare
    targetenrich = PhenoEnrichGenes(genesetcompare, database, organism, url = url)
    targetenrich = as.data.frame(targetenrich$htmltabla)
    nphenotargetall <- nrow(targetenrich)

    # Obtain the significative phenotypes terms from genesetcompare
    targetrelevant <- dplyr::filter(targetenrich, targetenrich$adjust_pvalue <= p.value)
    nphenotarget <- nrow(targetrelevant)

    # Obtain overlap
    phenounion <- full_join(enrichresult, targetenrich, by = "term_id")
    prunionn <- full_join(enrichresult,targetenrich, by = c("term_id","term_name","source"))
    nphenounion <- as.numeric(nrow(phenounion))

    # Inner phenotypes
    phenopresentall <- inner_join(enrichresult, targetenrich, by = "term_id") # Innner de fenotipos totales entre los dos datase
    ncompartidosall <- nrow(phenopresentall) # Número de fenotipos totales compartidos

    # RPOR
    scoreshared <- ncompartidosall/nphenounion

    # Obtain union sifnificative
    set1join <- dplyr::filter(prunionn, prunionn$adjust_pvalue.x <= p.value)

    set1join <-  set1join[,c(1:4,6:10,12:15)]
    names(set1join) <- c("term_id", "term_name","source", "adjust_pvalue_set1","gene_overlap_set1", "overlap_ratio_set1", "pvalue_set1", "common_genes_set1","adjust_pvalue_set2", "gene_overlap_set2", "overlap_ratio_set2", "pvalue_set2", "common_genes_set2")

    # Prepare for differentional analysis
    set1join$gene_overlap_set1[is.na(set1join$gene_overlap_set1)] <- 0
    set1join$gene_overlap_set2[is.na(set1join$gene_overlap_set2)] <- 0
    set1join[is.na(set1join)] <- 1

    # Obtain differentional phenotypes from geneset
    tabledif1u <- dplyr::filter(set1join, set1join$adjust_pvalue_set2 > p.value)

    # The same for genesetcomaper
    set2join <- dplyr::filter(prunionn, prunionn$adjust_pvalue.y <= p.value)

    set2join <-  set2join[,c(1:4,6:10,12:15)]
    names(set2join) <- c("term_id", "term_name", "source", "adjust_pvalue_set1","gene_overlap_set1", "overlap_ratio_set1", "pvalue_set1", "common_genes_set1","adjust_pvalue_set2", "gene_overlap_set2", "overlap_ratio_set2", "pvalue_set2", "common_genes_set2")

    set2join$gene_overlap_set1[is.na(set2join$gene_overlap_set1)] <- 0
    set2join$gene_overlap_set2[is.na(set2join$gene_overlap_set2)] <- 0
    set2join[is.na(set2join)] <- 1


    tabledif2u <- dplyr::filter(set2join, set2join$adjust_pvalue_set1 > p.value)

    # Order differentional phenotypes tables
    tabledif2u <- tabledif2u[order(tabledif2u$adjust_pvalue_set2),]
    tabledif1u <- tabledif1u[order(tabledif1u$adjust_pvalue_set1),]

    # Get fusion df term
    setfusion <- rbind(set1join,set2join)
    setfusion <- unique(setfusion)


    # get and comapre relevant phenotypes terms
    relevantesunion <- full_join(relevantes,targetrelevant, by = "term_id")
    runionn <- full_join(relevantes,targetrelevant, by = c("term_id","term_name", "source"))
    nrelevantesunion <- as.numeric(nrow(relevantesunion))
    relevantpresent <- inner_join(relevantes,targetrelevant, by = "term_id")
    ncompartidosr <- nrow(relevantpresent)

    # Obtain POR
    scoreenrich <- ncompartidosr/nrelevantesunion


    # Inner with all phenotypes
    targetenrichttr <- targetenrich[,c(1:4,6:9)]
    names(targetenrichttr) <- c("term_id", "term_name", "source", "adjust_pvalue_set2","gene_overlap_set2", "overlap_ratio_set2", "pvalue_set2", "common_genes_set2")

    # Table with phenotypes
    triucecompare <- merge.data.frame(enrichresult,targetenrichttr, by = c("term_id","term_name", "source"))

    # Label if the phenotype is relevant in both datasets
    triucecompare <- triucecompare %>%
      mutate(shared_enrichment = ifelse(adjust_pvalue_set2 <= p.value & adjust_pvalue <= p.value, "Yes", "No"))

    # All shared phenotypes
    triucecompare <- arrange(triucecompare, desc(shared_enrichment), adjust_pvalue_set2)

    targetrelevanttr <- targetrelevant[,c(1:4,6:9)]
    names(targetrelevanttr) <- c("term_id", "term_name", "source","adjust_pvalue_set2","gene_overlap_set2", "overlap_ratio_set2", "pvalue_set2", "common_genes_set2")

    triucefinal <- merge.data.frame(relevantes,targetrelevanttr, by = c("term_id","term_name", "source"))

    triucefinal <- triucefinal[order(triucefinal$adjust_pvalue_set),]

    nspor <- nrow(triucefinal)

    # Regression analysis
    regresiontarget <- stats::lm(gene_overlap_set1  ~ gene_overlap_set2, data = setfusion, na.action=na.exclude)
    ortarget <- summary(regresiontarget)


    # Preparation for simulation analysis
    # We need all genes
    genAll <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                           "/geneproteinlist.txt"))
    genAll <- as.data.frame(genAll)
    names(genAll) <- c("symbol","entrez")


    # We transform into a vector of entrez numbers
    genAll <- as.vector(genAll$entrez)

    # set seed
    set.seed(seed)

    # To split simulation analysis
    nulltestnumbernue =  nulltestnumber/2

    #We will keep a number of  a random set of genes with the same number of genes as the genes defined
    genesRandom <- replicate(nulltestnumbernue, sample(genAll, length(genesetcompare), replace=TRUE))
    genesRandomg2 <- replicate(nulltestnumbernue, sample(genAll, length(geneset), replace=TRUE))

    # Detect cores and work with a half of them
    nworkers <- (detectCores()/2)

    # Parallel simulation analysis
    cl <- makeForkCluster(nworkers)
    clusterEvalQ(cl, library(PhenoExamWeb))
    enrichrandomlist <- parApply(cl, genesRandom, 2, function(x) PhenoEnrichRandomOpt(x,database,organism))
    enrichrandomlistg2 <- parApply(cl, genesRandomg2, 2, function(x) PhenoEnrichRandomOpt(x,database,organism))
    stopCluster(cl)

    randomenrichrelevant <- lapply(enrichrandomlist, dplyr::filter, adjust_pvalue <= p.value)
    randomenrichrelevantg2 <- lapply(enrichrandomlistg2, dplyr::filter, adjust_pvalue <= p.value)

    # Intersección RPOR
    phenopresent <- lapply(enrichrandomlist, inner_join, x = enrichresult, by = "term_name")
    ncompartidos <- lapply(phenopresent, nrow)

    phenopresentg2 <- lapply(enrichrandomlistg2, inner_join, x = targetenrich, by = "term_name")
    ncompartidosg2 <- lapply(phenopresentg2, nrow)


    # Unión RPOR
    phenopresentunion <- lapply(enrichrandomlist, full_join, x = enrichresult, by = "term_name")
    ncompartidosunion <- lapply(phenopresentunion, nrow)

    phenopresentuniong2 <- lapply(enrichrandomlistg2, full_join, x = targetenrich, by = "term_name")
    ncompartidosuniong2 <- lapply(phenopresentuniong2, nrow)

    SharedScore <- mapply(function(x,y) (x/y), x = ncompartidos, y = ncompartidosunion, SIMPLIFY = F)
    SharedScoreg2 <- mapply(function(x,y) (x/y), x = ncompartidosg2, y = ncompartidosuniong2, SIMPLIFY = F)

    SharedScore <- c(SharedScore, SharedScoreg2)

    # Intersección POR
    colnames <- c("term_id", "term_name", "source", "adjust_pvalue","gene_associated_in_db",  "gene_overlap", "overlap_ratio", "pvalue")
    randomenrichrelevant <- lapply(randomenrichrelevant, setNames, colnames)
    relevantpresent <- lapply(randomenrichrelevant, inner_join, x = relevantes, by = "term_name")
    ncompartidosr <- lapply(relevantpresent, nrow)

    randomenrichrelevantg2 <- lapply(randomenrichrelevantg2, setNames, colnames)
    relevantpresentg2 <- lapply(randomenrichrelevantg2, inner_join, x = targetrelevant, by = "term_name")
    ncompartidosrg2 <- lapply(relevantpresentg2, nrow)

    # Unión POR
    relevantpresentunion <- lapply(randomenrichrelevant, full_join, x = relevantes, by = "term_name")
    ncompartidosrunion <- lapply(relevantpresentunion, nrow)

    relevantpresentuniong2 <- lapply(randomenrichrelevantg2, full_join, x = targetrelevant, by = "term_name")
    ncompartidosruniong2 <- lapply(relevantpresentuniong2, nrow)

    EnrichScore <- mapply(function(x,y) (x/y), x = ncompartidosr, y = ncompartidosrunion, SIMPLIFY = F)
    EnrichScoreg2 <- mapply(function(x,y) (x/y), x = ncompartidosrg2, y = ncompartidosruniong2, SIMPLIFY = F)

    EnrichScore <- c(EnrichScore, EnrichScoreg2)

  }


  if (conditional_case == T) {
    # Obtener el análisis de enriquecimiento del dataset de referencia
    referencia = PhenoEnrichGenes(genes=geneset, database, organism, url = url)
    enrichresult = as.data.frame(referencia$htmltabla)

    # Tomar los fenotipos estadisticamente significativos de los genes de referencia modulado con el p.value
    relevantes <- dplyr::filter(enrichresult, enrichresult$adjust_pvalue <= p.value)


    # Todos los genes de referencia en los que no exista overlap con el dataset comparado deben salir
    # de la ecuación
    targetenrich = PhenoEnrichCompare(geneset, genesetcompare, database, organism, url = url)
    targetenrich = as.data.frame(targetenrich$htmltabla)
    nphenotargetall <- nrow(targetenrich)
    # names(targetenrich) <- c("term_id", "term_name", "adjust_pvalue","gene_associated_in_db", "total_size_ratio", "gene_overlap", "input_size_ratio", "input_total_fold", "overlap_ratio", "pvalue", "common_genes_set2")


    # Tomar los fenotipos estadisticamente significativos
    targetrelevant <- dplyr::filter(targetenrich, targetenrich$adjust_pvalue <= p.value)
    nphenotarget <- nrow(targetrelevant)


    # Pregunta 1 overlap de términos aunque estos no sean relevantes
    phenounion <- full_join(enrichresult, targetenrich, by = "term_id")
    prunionn <- full_join(enrichresult,targetenrich, by = c("term_id","term_name", "source"))
    nphenounion <- as.numeric(nrow(phenounion))
    #nphenounion <- unique(phenounion$term_id)
    #nphenounion <- length(nphenounion) # Número de fenotipos totales UNICOS DE LA UNION
    phenopresentall <- inner_join(enrichresult, targetenrich, by = "term_id") # Innner de fenotipos totales entre los dos datase
    ncompartidosall <- nrow(phenopresentall) # Número de fenotipos totales compartidos

    scoreshared <- ncompartidosall/nphenounion

    # Quedarme con los 0.05 de uno con la unión al otro
    set1join <- dplyr::filter(prunionn, prunionn$adjust_pvalue.x <= p.value)

    set1join <-  set1join[,c(1:4,6:10,12:15)]
    names(set1join) <- c("term_id", "term_name", "source", "adjust_pvalue_set1","gene_overlap_set1", "overlap_ratio_set1", "pvalue_set1", "common_genes_set1","adjust_pvalue_set2", "gene_overlap_set2", "overlap_ratio_set2", "pvalue_set2", "common_genes_set2")

    set1join$gene_overlap_set1[is.na(set1join$gene_overlap_set1)] <- 0
    set1join$gene_overlap_set2[is.na(set1join$gene_overlap_set2)] <- 0
    set1join[is.na(set1join)] <- 1

    tabledif1u <- dplyr::filter(set1join, set1join$adjust_pvalue_set2 > p.value)

    # Quedarme con los 0.05 del otro sin importar lo del otro



    # Quedarme con los 0.05 del otro sin importar lo del otro

    set2join <- dplyr::filter(prunionn, prunionn$adjust_pvalue.y <= p.value)

    set2join <-  set2join[,c(1:4,6:10,12:15)]
    names(set2join) <- c("term_id", "term_name", "source", "adjust_pvalue_set1","gene_overlap_set1", "overlap_ratio_set1", "pvalue_set1", "common_genes_set1","adjust_pvalue_set2", "gene_overlap_set2", "overlap_ratio_set2", "pvalue_set2", "common_genes_set2")

    set2join$gene_overlap_set1[is.na(set2join$gene_overlap_set1)] <- 0
    set2join$gene_overlap_set2[is.na(set2join$gene_overlap_set2)] <- 0
    set2join[is.na(set2join)] <- 1


    tabledif2u <- dplyr::filter(set2join, set2join$adjust_pvalue_set1 > p.value)



    tabledif2u <- tabledif2u[order(tabledif2u$adjust_pvalue_set2),]
    tabledif1u <- tabledif1u[order(tabledif1u$adjust_pvalue_set1),]

    # Unir estos dos datasets
    setfusion <- rbind(set1join,set2join)
    setfusion <- unique(setfusion)


    # Comparación de fenotipos enriquecidos en ambos dataset, número de compartidos
    relevantesunion <- full_join(relevantes,targetrelevant, by = "term_id")
    nrelevantesunion <- as.numeric(nrow(relevantesunion))
    relevantpresent <- inner_join(relevantes,targetrelevant, by = "term_id")
    ncompartidosr <- nrow(relevantpresent)


    scoreenrich <- ncompartidosr/nrelevantesunion


    # Tabla con el inner de todos los fenotipos
    targetenrichttr <- targetenrich[,c(1:4,6:9)]
    names(targetenrichttr) <- c("term_id", "term_name", "source", "adjust_pvalue_set2","gene_overlap_set2", "overlap_ratio_set2", "pvalue_set2", "common_genes_set2")

    triucecompare <- merge.data.frame(enrichresult,targetenrichttr, by = c("term_id","term_name","source"))

    # Etiqueta condicional
    triucecompare <- triucecompare %>%
      mutate(shared_enrichment = ifelse(adjust_pvalue_set2 <= p.value & adjust_pvalue <= p.value, "Yes", "No"))

    # Aqui están todos los fenotipos compartidos, ordenados por enriquecimientos en ambos
    triucecompare <- arrange(triucecompare, desc(shared_enrichment), adjust_pvalue_set2)


    targetrelevanttr <- targetrelevant[,c(1:4,6:9)]
    names(targetrelevanttr) <- c("term_id", "term_name", "source", "adjust_pvalue_set2","gene_overlap_set2", "overlap_ratio_set2", "pvalue_set2", "common_genes_set2")

    triucefinal <- merge.data.frame(relevantes,targetrelevanttr, by = c("term_id","term_name", "source"))

    triucefinal <- triucefinal[order(triucefinal$adjust_pvalue_set),]
    nspor <- nrow(triucefinal)

    # Esto nos da que cuanto más enriquecido en un sitio más enriquecido en el otro sitio
    regresiontarget <- stats::lm(gene_overlap_set1  ~ gene_overlap_set2, data = setfusion, na.action=na.exclude)
    ortarget <- summary(regresiontarget)





    if (organism == "human") {
      nonoverlapgenes <- foundgeneoverlap(geneset, genesetcompare)
    }
    if (organism == "mouse") {
      targetgenesm <- as.data.frame(genesetcompare)
      names(targetgenesm) <- "mouse_symbol"
      targetgenesm$mouse_symbol <- as.character(targetgenesm$mouse_symbol)

      # Read database
      homologia <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                "/homology.csv"), header = T)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(targetgenesm, homologia, by = "mouse_symbol")
      onlygenes <- onlygenes$human_symbol
      onlygenes <- as.data.frame(onlygenes)
      names(onlygenes) <- "symbol"
      onlygenes$symbol <- as.character(onlygenes$symbol)
      targetgenesm = onlygenes

      referencegenesm <- as.data.frame(geneset)
      names(referencegenesm) <- "mouse_symbol"
      referencegenesm$mouse_symbol <- as.character(referencegenesm$mouse_symbol)

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(referencegenesm, homologia, by = "mouse_symbol")
      onlygenes <- onlygenes$human_symbol
      onlygenes <- as.data.frame(onlygenes)
      names(onlygenes) <- "symbol"
      onlygenes$symbol <- as.character(onlygenes$symbol)
      referencegenesm = onlygenes
      nonoverlapgenes <- foundgeneoverlap(referencegenesm, targetgenesm)
    }

    genAll <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                           "/geneproteinlist.txt"))
    genAll <- as.data.frame(genAll)
    names(genAll) <- c("symbol","entrez")


    # We will keep only the selected genes
    genAll <- dplyr::anti_join(genAll, nonoverlapgenes, by = "entrez")
    genAll <- as.vector(genAll$entrez)

    set.seed(seed)

    #We will keep a number of  a random set of genes with the same number of genes as the genes defined.
    nulltestnumbernue =  nulltestnumber/2
    genesRandom <- replicate(nulltestnumbernue, sample(genAll, length(genesetcompare), replace=TRUE))
    genesRandomg2 <- replicate(nulltestnumbernue, sample(genAll, length(geneset), replace=TRUE))

    # Detecto los corazones y utilizo la mitad
    nworkers <- (detectCores()/2)


    # ME HE QUEDADO CON PHENOENRICHCOMPARERANDOM
    # Paralelizo la opreación de calcular en enriquecimiento para cada set de genes aleatorios
    cl <- makeForkCluster(nworkers)
    clusterEvalQ(cl, library(PhenoExamWeb))
    enrichrandomlist <- parApply(cl, genesRandom, 2, function(x) PhenoEnrichCompareRandom(nonoverlapgenes, geneset, x, database,organism))
    enrichrandomlistg2 <- parApply(cl, genesRandomg2, 2, function(x) PhenoEnrichCompareRandom(nonoverlapgenes, geneset, x,database,organism))
    stopCluster(cl)


    randomenrichrelevant <- lapply(enrichrandomlist, dplyr::filter, adjust_pvalue <= p.value)
    randomenrichrelevantg2 <- lapply(enrichrandomlistg2, dplyr::filter, adjust_pvalue <= p.value)

    # Intersección RPOR
    phenopresent <- lapply(enrichrandomlist, inner_join, x = enrichresult, by = "term_name")
    ncompartidos <- lapply(phenopresent, nrow)

    phenopresentg2 <- lapply(enrichrandomlistg2, inner_join, x = targetenrich, by = "term_name")
    ncompartidosg2 <- lapply(phenopresentg2, nrow)


    # Unión RPOR
    phenopresentunion <- lapply(enrichrandomlist, full_join, x = enrichresult, by = "term_name")
    ncompartidosunion <- lapply(phenopresentunion, nrow)

    phenopresentuniong2 <- lapply(enrichrandomlistg2, full_join, x = targetenrich, by = "term_name")
    ncompartidosuniong2 <- lapply(phenopresentuniong2, nrow)

    SharedScore <- mapply(function(x,y) (x/y), x = ncompartidos, y = ncompartidosunion, SIMPLIFY = F)
    SharedScoreg2 <- mapply(function(x,y) (x/y), x = ncompartidosg2, y = ncompartidosuniong2, SIMPLIFY = F)

    SharedScore <- c(SharedScore, SharedScoreg2)


    # Intersección POR
    colnames <- c("term_id", "term_name", "source", "adjust_pvalue","gene_associated_in_db",  "gene_overlap", "overlap_ratio", "pvalue")
    randomenrichrelevant <- lapply(randomenrichrelevant, setNames, colnames)
    relevantpresent <- lapply(randomenrichrelevant, inner_join, x = relevantes, by = "term_name")
    ncompartidosr <- lapply(relevantpresent, nrow)

    randomenrichrelevantg2 <- lapply(randomenrichrelevantg2, setNames, colnames)
    relevantpresentg2 <- lapply(randomenrichrelevantg2, inner_join, x = targetrelevant, by = "term_name")
    ncompartidosrg2 <- lapply(relevantpresentg2, nrow)

    # Unión POR
    relevantpresentunion <- lapply(randomenrichrelevant, full_join, x = relevantes, by = "term_name")
    ncompartidosrunion <- lapply(relevantpresentunion, nrow)

    relevantpresentuniong2 <- lapply(randomenrichrelevantg2, full_join, x = targetrelevant, by = "term_name")
    ncompartidosruniong2 <- lapply(relevantpresentuniong2, nrow)

    EnrichScore <- mapply(function(x,y) (x/y), x = ncompartidosr, y = ncompartidosrunion, SIMPLIFY = F)
    EnrichScoreg2 <- mapply(function(x,y) (x/y), x = ncompartidosrg2, y = ncompartidosruniong2, SIMPLIFY = F)

    EnrichScore <- c(EnrichScore, EnrichScoreg2)



  }

  # We will keep with the phenoratios from random genes greater than the phenoratios from predictions genes
  # EnrichScore
  EnrichScoreMayores = which(EnrichScore > scoreenrich)
  EnrichScoreMayoresL = as.numeric(length(EnrichScoreMayores))

  # SharedScore
  SharedScoreMayores = which(SharedScore > scoreshared)
  SharedScoreMayoresL = as.numeric(length(SharedScoreMayores))

  # We are going to calculate the pvalue
  pvalorEnrichScore = (1 + EnrichScoreMayoresL) / (1 + as.numeric(length(EnrichScore)))
  pvalorSharedScoreMayores = (1 + SharedScoreMayoresL) / (1 + as.numeric(length(SharedScore)))


  # Plot  Relaxed Phenotypic Overlap Ratio (RPOR)
  ratiosRandomHP2 <- unlist(SharedScore, use.names = FALSE)
  pr <- as.data.frame(ratiosRandomHP2)
  rpre <- as.data.frame(scoreshared)
  pl1 <- ggplot2::ggplot(pr, ggplot2::aes(x= ratiosRandomHP2)) + ggplot2::geom_histogram(binwidth= mean(ratiosRandomHP2)/30, position="identity", alpha=0.7, fill="red") + ggplot2::geom_vline( ggplot2::aes(xintercept= scoreshared),
                                                                                                                                                                                                color="blue", linetype="dashed", size=0.7)+
    labs(title="Relaxed Phenotypic Overlap Ratio (RPOR) from random genes vs geneset2",x= "Relaxed Phenotypic Overlap Ratio (RPOR)", y = "Frecuency in random gene sets")

  a <- ggplot2::ggplot_build(pl1)$layout$panel_scales_y[[1]]$range$range

  plotshared <- pl1 +  ggplot2::geom_text( ggplot2::aes(scoreshared-(scoreshared/25), mean(a), label = "Relaxed Phenotypic Overlap Ratio (POR) from", vjust=0,angle=90, family="Times")) +  ggplot2::geom_text( ggplot2::aes(scoreshared-(scoreshared/55), mean(a) ,label = "geneset2", vjust=0,angle=90, family="Times"))


  # Plot Phenotype Overlap Ratio (POR)
  ratiosRandomHP2 <- unlist(EnrichScore, use.names = FALSE)

  pr <- as.data.frame(ratiosRandomHP2)
  rpre <- as.data.frame(scoreenrich)
  pl1 <- ggplot2::ggplot(pr, ggplot2::aes(x= ratiosRandomHP2)) + ggplot2::geom_histogram(binwidth= 0.005, position="identity", alpha=0.7, fill="red") + ggplot2::geom_vline( ggplot2::aes(xintercept= scoreenrich),
                                                                                                                                                                             color="blue", linetype="dashed", size=0.7)+
    labs(title="Phenotype Overlap Ratio (POR)
 from random genes vs geneset2",x= "Phenotype Overlap Ratio (POR)
", y = "Frecuency in random gene sets")

  b <- ggplot2::ggplot_build(pl1)$layout$panel_scales_y[[1]]$range$range

  plotenrichs <- pl1 +  ggplot2::geom_text( ggplot2::aes(scoreenrich-(scoreenrich/40), mean(b) ,label = "POR from geneset2",vjust=0,angle=90, family="Times"))




  # Interactive plot

  pdif <- setfusion %>%  dplyr::mutate(Enrich = case_when( setfusion$adjust_pvalue_set1 <= 0.05 & setfusion$adjust_pvalue_set2 <= 0.05  ~ "SharedEnrich",
                                                           setfusion$adjust_pvalue_set1 > 0.05  ~ "Relevant in geneset2",
                                                           setfusion$adjust_pvalue_set2 > 0.05  ~ "Relevant in geneset1" )) %>%

    # prepare text for tooltip
    dplyr::mutate(text = paste("term_id: ", term_id, "\nterm_name: ", term_name, "\npvalue_set1: ", adjust_pvalue_set1, "\npvalue_set2: ", adjust_pvalue_set2, "\nGeneassociation_geneset1: ", gene_overlap_set1, "\nGeneassociation_geneset2: ", gene_overlap_set2,  sep="")) %>%

    # Classic ggplot
    ggplot2::ggplot( ggplot2::aes(x=gene_overlap_set2, y=gene_overlap_set1,  color = Enrich, text=text)) +
    ggplot2::theme(text = element_text(size=8)) +
    ggplot2::geom_point(alpha = 0.5) +
    #viridis::scale_color_viridis(discrete=TRUE, guide=FALSE) +
    #ggplot2::theme(legend.position="none") +
    labs(title = "Phenotype relevance association analysis for gene sets", x = "Number of genes associated in gene set2", y = "Number of genes associated in gene set1") +
    theme(plot.title = element_text(size = 15),
          axis.title.x = element_text(size = 10),
          axis.title.y = element_text(size = 10))


  # turn ggplot interactive with plotly
  ppdif <- plotly::ggplotly(pdif, tooltip="text", parse = T) %>%
    add_lines(x = ~gene_overlap_set2, y = fitted(regresiontarget), name = "Regression line")





  if (pvalorEnrichScore <= 0.05) {
    mensaje <-  paste0("Gene set2 and gene set1 shared ",ncompartidosall, " phenotypic terms (out of ",nphenounion, " unique phenotypic terms in both), that yields a Relaxed Phenotypic Overlap Ratio (RPOR) of ",round(scoreshared,3), " (p < ",round(pvalorSharedScoreMayores,6),"). They shared ",nspor, " significant phenotypic terms (out of ",nrelevantesunion, " unique significant phenotypic terms in both), that yields a Phenotypic Overlap Ratio (POR) of ",round(scoreenrich,3), " (p < ",round(pvalorEnrichScore,6),"). Phenotype relevance association analysis for gene sets (i.e. whether the shared phenotypes are similar in relevance, i.e. in the number of genes associated with them, within each gene set) results in an adjusted R squared of ",round(ortarget$adj.r.squared,3)," ( p < ",ortarget$coefficients[2,4],") which suggests that an important portion of the common phenotypes are similar in relevance.  The p-values were obtained through randomization of ",nulltestnumber, " random gene sets.  Gene set 1 and gene set 2 are statistically significantly similar to each other in phenotypic terms.")
  }
  if (pvalorEnrichScore > 0.05) {
    mensaje <-  paste0("Gene set2 and gene set1 shared ",ncompartidosall, " phenotypic terms (out of ",nphenounion, " unique phenotypic terms in both), that yields a Relaxed Phenotypic Overlap Ratio (RPOR) of ",round(scoreshared,3), " (p < ",round(pvalorSharedScoreMayores,6),"). They shared ",nspor, " significant phenotypic terms (out of ",nrelevantesunion, " unique significant phenotypic terms in both), that yields a Phenotypic Overlap Ratio (POR) of ",round(scoreenrich,3), " (p < ",round(pvalorEnrichScore,6),"). Phenotype relevance association analysis for gene sets (i.e. whether the shared phenotypes are similar in relevance, i.e. in the number of genes associated with them, within each gene set) results in an adjusted R squared of ",round(ortarget$adj.r.squared,3)," ( p < ",ortarget$coefficients[2,4],") .  The p-values were obtained through randomization of ",nulltestnumber, " random gene sets.  Gene set 1 and gene set 2 are not statistically significantly similar to each other in phenotypic terms.")
  }

  newList <- list("phenomessage" = mensaje, "data.enrich" = triucefinal, "datatotal" = triucecompare, "RPOR" = plotshared, "POR" = plotenrichs,  "randomvalues" = pr,  "lm" = ortarget, "r.squared" = round(ortarget$adj.r.squared,5), "lmp.value" = ortarget$coefficients[2,4],  "plotdif" = ppdif, "tabledif1" = tabledif1u, "tabledif2" = tabledif2u)

  return(newList)
}

#' @title PhenoEnrichGenes
#' @description This function do a phenotype enrichment analysis and obtain a table and a graph
#' @param genes A vector with human gene symbol.
#' @param database A string paramater with different options. You can define a vector with the databases.
#' @param organism A string paramater: "human" if you have human genes and "mouse" if you have mouse genes
#' @param plotn An integer with the number of term that you want in the plot. The default value is 30.
#' @param url A logical paramater. T if you want interactive links
#' @details This function has developed by Alejandro Cisterna García as part of his PhD mentored by Juan Antonio Botía Blaya
#' @export





PhenoEnrichGenes = function(genes, database = "HPO", organism = "human", plotn=30, url = F){

  if (organism == "human") {

    # Make two empty df with names
    rbjointri = data.frame(matrix(vector(), 0, 9,
                                  dimnames=list(c(), c("term_id", "term_name", "source", "adjust_pvalue",
                                                       "genes_associated_in_db","gene_overlap", "overlap_ratio"
                                                       ,"pvalue","common_genes"))),
                           stringsAsFactors=F)
    rbjoinhtml <-rbjointri

    # For each database name in a vector run its loadder
    for (db in database) {

      # Get the loadder name
      getnamedb <- paste0("",db,"loadder")

      # Run the loadder
      peaload <- get(getnamedb)(genes,organism = "human", plotnumber = plotn, url = url)

      # join data frames generated
      rbjoinhtml <- rbind(rbjoinhtml, peaload$htmltabla)
      rbjointri <- rbind(rbjointri, peaload$alldata)

      # Order by pvalue
      rbjoinhtml = rbjoinhtml[order(rbjoinhtml$adjust_pvalue),] # htmltabla
      rbjointri = rbjointri[order(rbjointri$adjust_pvalue),] # alldata

      # To plot
      triucu = rbjointri[1:plotn,]
    }
  }

  if (organism == "mouse") {

    genes <- as.data.frame(genes)
    names(genes) <- "mouse_symbol"
    genes$mouse_symbol <- as.character(genes$mouse_symbol)

    # Read database
    homologia <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                              "/homology.csv"), header = T)

    # We will keep only the selected genes
    onlygenes <- dplyr::inner_join(genes, homologia, by = "mouse_symbol")
    onlygenes <- onlygenes$human_symbol
    onlygenes <- as.data.frame(onlygenes)
    names(onlygenes) <- "symbol"
    onlygenes$symbol <- as.character(onlygenes$symbol)
    genes = unique(onlygenes)
    genes = genes$symbol

    # Make two empty df with names
    rbjointri = data.frame(matrix(vector(), 0, 9,
                                  dimnames=list(c(), c("term_id", "term_name", "source", "adjust_pvalue",
                                                       "genes_associated_in_db","gene_overlap", "overlap_ratio"
                                                       ,"pvalue","common_genes"))),
                           stringsAsFactors=F)
    rbjoinhtml <-rbjointri

    # For each database name in a vector run its loadder
    for (db in database) {

      # Get the loadder name
      getnamedb <- paste0("",db,"loadder")

      # Run the loadder
      peaload <- get(getnamedb)(genes,organism = "human", plotnumber = plotn,  url = url)

      # join data frames generated
      rbjoinhtml <- rbind(rbjoinhtml, peaload$htmltabla)
      rbjointri <- rbind(rbjointri, peaload$alldata)

      # Order by pvalue
      rbjoinhtml = rbjoinhtml[order(rbjoinhtml$adjust_pvalue),] # htmltabla
      rbjointri = rbjointri[order(rbjointri$adjust_pvalue),] # alldata

      # To plot
      triucu = rbjointri[1:plotn,]
    }
  }

  # Esto es para que en la generación del gráfico no salga el no fenotipo
  #triucu <- filter(triucu, triucu$term_id != "CRB:XXX")
  #triucu <- filter(triucu, triucu$term_id != "wXXX") no pasa nada si

  # To plot
  p <- triucu %>%

    # prepare text for tooltip
    dplyr::mutate(text = paste("ID Number: ", term_id, "\nTerm: ", term_name,  "\nGene Ratio: ", overlap_ratio, "\nAdjust pvalue: ", adjust_pvalue, "\nCommon genes: ", common_genes)) %>%
    # Classic ggplot
    ggplot(  ggplot2::aes(x= gene_overlap , y= reorder(paste0("",term_id,": ",term_name,""), -adjust_pvalue),  fill = adjust_pvalue, text=text)) +
    theme (text = element_text(size= 8)) +
    geom_bar(stat="identity", alpha=0.7) +
    scale_fill_gradientn(colours = rainbow(2), trans= "log") +
    theme(legend.position="right") +
    labs(title = "Phenotype Enrichment Analysis", x = "Number of genes associated with the term", y = "", fill = "p.value") + #
    theme(plot.title = element_text(size = 15),
          axis.title.x = element_text(size = 10),
          axis.title.y = element_text(size = 10),
          axis.text = element_text(
            angle = 0))

  # turn ggplot interactive with plotly layout.title
  nuevvogg <- ggplotly(p, tooltip="text")  %>%
    config(mathjax = "cdn",
           toImageButtonOptions = list(
             format = "svg",
             filename = "myplot",
             width = 1000,
             height = 600
           )
    )

  newList <- list("alldata" = rbjointri, "htmltabla" = rbjoinhtml,   "graph" = nuevvogg)
  return(newList)


}




#' @title PhenoEnrichRandomOpt
#' @description This function makes a plot with enriched phenotype info.
#' @param genes A vector with human gene symbol.
#' @param database A string paramater with different options. You can define a vector with the databases.
#' @param organism A string paramater: "human" if you have human genes and "mouse" if you have mouse genes
#' @param plotn An integer with the number of term that you want in the plot. The default value is 30.
#' @details This function has developed by Alejandro Cisterna García as part of his PhD mentored by Juan Antonio Botía Blaya
#' @export



PhenoEnrichRandomOpt = function(genes,  database = "HPO", organism = "human"){


  if (organism == "human") {

    # Make one empty df with names
    rbjointri = data.frame(matrix(vector(), 0, 8,
                                  dimnames=list(c(), c("term_id", "term_name", "source", "adjust_pvalue",
                                                       "genes_associated_in_db","gene_overlap", "overlap_ratio"
                                                       ,"pvalue"))),
                           stringsAsFactors=F)


    # For each database name in a vector run its loadder
    for (db in database) {

      # Get the loadder name
      getnamedb <- paste0("",db,"loadderOpt")

      # Run the loadder
      peaload <- get(getnamedb)(genes,organism = "human", plotnumber = plotn)

      # join data frames generated
      rbjointri <- rbind(rbjointri, peaload)

      # Order by pvalue
      rbjointri = rbjointri[order(rbjointri$adjust_pvalue),] # alldata

    }
  }



  if (organism == "mouse") {
    genes <- as.data.frame(genes)
    names(genes) <- "mouse_symbol"
    genes$mouse_symbol <- as.character(genes$mouse_symbol)

    # Read database
    homologia <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                              "/homology.csv"), header = T)

    # We will keep only the selected genes
    onlygenes <- dplyr::inner_join(genes, homologia, by = "mouse_symbol")
    onlygenes <- onlygenes$human_symbol
    onlygenes <- as.data.frame(onlygenes)
    names(onlygenes) <- "symbol"
    onlygenes$symbol <- as.character(onlygenes$symbol)
    genes = unique(onlygenes)
    genes = genes$symbol


    # Make one empty df with names
    rbjointri = data.frame(matrix(vector(), 0, 8,
                                  dimnames=list(c(), c("term_id", "term_name", "source", "adjust_pvalue",
                                                       "genes_associated_in_db","gene_overlap", "overlap_ratio"
                                                       ,"pvalue"))),
                           stringsAsFactors=F)


    # For each database name in a vector run its loadder
    for (db in database) {

      # Get the loadder name
      getnamedb <- paste0("",db,"loadderOpt")

      # Run the loadder
      peaload <- get(getnamedb)(genes,organism = "human", plotnumber = plotn)

      # join data frames generated
      rbjointri <- rbind(rbjointri, peaload)

      # Order by pvalue
      rbjointri = rbjointri[order(rbjointri$adjust_pvalue),] # alldata

    }


  }

  newList <- rbjointri
  return(newList)
}



#' @title convertgenesymbolmessage
#' @description This function give a message with the homologues map
#' @param genes A vector with gene symbol.
#' @param organism A string paramater: "human" if you have human genes and "mouse if you have mouse genes
#' @details This function has developed by Alejandro Cisterna García as part of his PhD mentored by Juan Antonio Botía Blaya
#' @export


convertgenesymbolmessage = function(genes, organism = "human"){

  if (organism == "human") {

    geneshomo <- as.data.frame(genes)
    names(geneshomo) <- "human_symbol"
    geneshomo$human_symbol <- as.character(geneshomo$human_symbol)
    geneshomo <- unique(geneshomo)
    ngeneshomo <- nrow(geneshomo)

    # Read database
    homologia <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                              "/homology.csv"), header = T)

    # We will keep only the selected genes
    onlygeneshomo <- dplyr::anti_join(geneshomo, homologia, by = "human_symbol")
    onlygeneshomo <- onlygeneshomo$human_symbol
    nonly <- length(onlygeneshomo)
    onlygeneshomo <- paste(onlygeneshomo,collapse=" ")


    porcenta <- round(((nonly/ngeneshomo)*100), digits = 2)


    if (porcenta == 0) {
      mensaje <-  paste0("All genes from the input have mice homologues genes")
    }
    if (porcenta != 0) {
      mensaje <-  paste0("PhenoExamWeb could not find homologues of the human genes: ", onlygeneshomo," (",nonly,"/",ngeneshomo," genes) ",porcenta,"% of the input genes")

    }
    return(mensaje)
  }

  if (organism == "mouse") {
    genes <- as.data.frame(genes)
    names(genes) <- "mouse_symbol"
    genes$mouse_symbol <- as.character(genes$mouse_symbol)
    genes <- unique(genes)
    ngenes <- nrow(genes)

    # Read database
    homologia <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                              "/homology.csv"), header = T)

    # We will keep only the selected genes
    onlygenes <- dplyr::anti_join(genes, homologia, by = "mouse_symbol")
    onlygenes <- onlygenes$mouse_symbol
    nonly <- length(onlygenes)
    onlygenes <- paste(onlygenes,collapse=" ")


    porcenta <- round(((nonly/ngenes)*100), digits = 2)

    mensaje <-  paste0("PhenoExamWeb could not find homologues of the mouse genes: ", onlygenes," (",nonly,"/",ngenes," genes) ",porcenta,"% of the input genes")

    return(mensaje)
  }
}


#' @title entrezmap
#' @description This function give a message with the entrez map
#' @param genes A vector with gene symbol.
#' @param human A logical paramater if you have human gene symbol
#' @details This function has developed by Alejandro Cisterna García as part of his PhD mentored by Juan Antonio Botía Blaya
#' @export


entrezmap = function(genes, human = T){

  if (human) {
    mouse = F
    # Covert vector to a daframe
    genesentrez <- as.data.frame(genes)
    names(genesentrez) <- "Symbol"
    ngenes <- nrow(genesentrez)

    # Pasar estos genes a entrezid clusterprofiler
    genes.df <- bitr(genesentrez$Symbol, fromType = "SYMBOL",
                     toType = c("SYMBOL", "ENTREZID"),
                     OrgDb = "org.Hs.eg.db")
    genesentrez <- unique(genes.df)
    names(genesentrez) <- c("symbol","entrez")
    genesentrez$entrez <- as.numeric(as.character(genesentrez$entrez))

    # Read database
    custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                           "/geneproteinlist.txt"), header = T)
    names(custom) <- c("symbol","entrez")

    # We will keep only the selected genes
    onlygenesentrez <- dplyr::anti_join(genesentrez, custom, by = "entrez")

    # Ahora si tengo los dos y podría mostrar que genes no se mapean o entrar dentro
    symyentrez <- unique(onlygenesentrez[,1:2])

    genesnoentrez <- symyentrez$symbol
    nonly <- as.numeric(length(genesnoentrez))
    genesnoentrez <- paste(genesnoentrez,collapse=" ")


    porcenta <- round(((nonly/ngenes)*100), digits = 2)

    if (porcenta == 0) {
      mensaje <-  paste0("All genes from the input will be used")
    }
    if (porcenta != 0) {
      mensaje <-  paste0("PhenoExamWeb could not use or find these genes: ", genesnoentrez," (",nonly,"/",ngenes," genes) ",porcenta,"% of the input genes")

    }

    return(mensaje)
  }
}


#' @title PhenoEnrichCompare
#' @description This function is used for RandomComparePheno.
#' @param genes A vector with human gene symbol.
#' @param database A string paramater with different options. You can define a vector with the databases.
#' @param organism A string paramater: "human" if you have human genes and "mouse" if you have mouse genes
#' @param plotn An integer with the number of term that you want in the plot. The default value is 30.
#' @param url A logical paramater. T if you want interactive links
#' @details This function has developed by Alejandro Cisterna García as part of his PhD mentored by Juan Antonio Botía Blaya
#' @export




# Función para obtener el enriquecimiento de términos HPO Y CRB
PhenoEnrichCompare = function(geneset, genesetcompare, database = "HPO", organism = "human", plotn=30, plotnumber = 1:plotn, url = F){




  if (organism == "human") {


    # Find gene overlap
    onlygenes <- foundgeneoverlap(geneset, genesetcompare)


    # Make two empty df with names
    rbjointri = data.frame(matrix(vector(), 0, 9,
                                  dimnames=list(c(), c("term_id", "term_name", "source", "adjust_pvalue",
                                                       "genes_associated_in_db","gene_overlap", "overlap_ratio"
                                                       ,"pvalue","common_genes"))),
                           stringsAsFactors=F)
    rbjoinhtml <-rbjointri

    # For each database name in a vector run its loadder
    for (db in database) {

      # Get the loadder name
      getnamedb <- paste0("",db,"loaddercompare")

      # Run the loadder
      peaload <- get(getnamedb)(onlygenes, genesetcompare,organism = "human", plotnumber = plotn, url = url)

      # join data frames generated
      rbjoinhtml <- rbind(rbjoinhtml, peaload$htmltabla)
      rbjointri <- rbind(rbjointri, peaload$alldata)

      # Order by pvalue
      rbjoinhtml = rbjoinhtml[order(rbjoinhtml$adjust_pvalue),] # htmltabla
      rbjointri = rbjointri[order(rbjointri$adjust_pvalue),] # alldata

      # To plot
      triucu = rbjointri[1:plotn,]
    }
  }



  if (organism == "mouse") {
    genes <- as.data.frame(genesetcompare)
    names(genes) <- "mouse_symbol"
    genes$mouse_symbol <- as.character(genes$mouse_symbol)

    # Read database
    homologia <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                              "/homology.csv"), header = T)

    # We will keep only the selected genes
    onlygenes <- dplyr::inner_join(genes, homologia, by = "mouse_symbol")
    onlygenes <- onlygenes$human_symbol
    onlygenes <- as.data.frame(onlygenes)
    names(onlygenes) <- "symbol"
    onlygenes$symbol <- as.character(onlygenes$symbol)
    genesetcompare = unique(onlygenes)

    genesr <- as.data.frame(geneset)
    names(genesr) <- "mouse_symbol"
    genesr$mouse_symbol <- as.character(genesr$mouse_symbol)

    # We will keep only the selected genes
    onlygenes <- dplyr::inner_join(genesr, homologia, by = "mouse_symbol")
    onlygenes <- onlygenes$human_symbol
    onlygenes <- as.data.frame(onlygenes)
    names(onlygenes) <- "symbol"
    onlygenes$symbol <- as.character(onlygenes$symbol)
    genesr = unique(onlygenes)


    # Encontrar el no overlap entre unos y otros
    onlygenes <- foundgeneoverlap(genesr, genesetcompare)


    # Make two empty df with names
    rbjointri = data.frame(matrix(vector(), 0, 9,
                                  dimnames=list(c(), c("term_id", "term_name", "source", "adjust_pvalue",
                                                       "genes_associated_in_db","gene_overlap", "overlap_ratio"
                                                       ,"pvalue","common_genes"))),
                           stringsAsFactors=F)
    rbjoinhtml <-rbjointri

    # For each database name in a vector run its loadder
    for (db in database) {

      # Get the loadder name
      getnamedb <- paste0("",db,"loaddercompare")

      # Run the loadder
      peaload <- get(getnamedb)(onlygenes, genesetcompare,organism = "human", plotnumber = plotn, url = url)

      # join data frames generated
      rbjoinhtml <- rbind(rbjoinhtml, peaload$htmltabla)
      rbjointri <- rbind(rbjointri, peaload$alldata)

      # Order by pvalue
      rbjoinhtml = rbjoinhtml[order(rbjoinhtml$adjust_pvalue),] # htmltabla
      rbjointri = rbjointri[order(rbjointri$adjust_pvalue),] # alldata

      # To plot
      triucu = rbjointri[1:plotn,]
    }





  }

  newList <- list("alldata" = rbjointri, "htmltabla" = rbjoinhtml)
  return(newList)
}





#' @title PhenoEnrichGenespran
#' @description This function makes a plot with enriched phenotype info.
#' @param genes A vector with human gene symbol.
#' @param database A string paramater with different options. You can define a vector with the databases.
#' @param organism A string paramater: "human" if you have human genes and "mouse" if you have mouse genes
#' @param plotn An integer with the number of term that you want in the plot. The default value is 30.
#' @details This function has developed by Alejandro Cisterna García as part of his PhD mentored by Juan Antonio Botía Blaya
#' @export




# Función para obtener el enriquecimiento de términos HPO Y CRB
PhenoEnrichGenespran = function(genes, database = "HPO", organism = "human", plotn=30, plotnumber = 1:plotn){

  if (organism == "human") {

    # Si solo HPO
    if (database == "HPO") {


      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/hpodatatotales.csv"), header = T)


      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes, database = "HPO", organism)


      # Make total_size_ratio per total
      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal


      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))


      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19248, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19248, length(genes), lower.tail = FALSE))


      triucusin = triucusin[order(triucusin$adjust_pvalue),]

      triucusin = triucusin[,c(1:2,6,3:5,7)]

      qglist <- QueryGenes(genes,database = "HPO", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))



      listadegenesqg <- unique(listadegenesqg[,c(3,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

      # Esto es para decidir el número de términos para plotear
      triucu = triucusin[plotnumber,]


    }

    # Si solo CRB
    if (database == "CRB") {
      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/cbrtotalnumber.csv"), header = T)
      names(numportertotal) = c("term_id",   "term_name", "genes_associated_in_db")


      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes,database = "CRB",organism)

      names(panel) = c( "term_id",   "term_name", "gene_overlap")

      # Make total_size_ratio per total
      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal



      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))




      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)



      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19274, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19274, length(genes), lower.tail = FALSE))


      triucusin = triucusin[order(triucusin$adjust_pvalue),]

      triucusin = triucusin[,c(1:2,6,3:5,7)]

      qglist <- QueryGenes(genes,database = "CRB", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(4,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

      # Esto es para decidir el número de términos para plotear
      triucu = triucusin[plotnumber,]

    }

    # Si solo MGD
    if (database == "MGD"  | database == "MOUSEDB"){

      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/totalmponumber.tsv"), header = T, sep = "\t")


      names(numportertotal) <- c("term_id", "term_name", "genes_associated_in_db")

      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes, database = "MGD",organism)


      # Make total_size_ratio per total
      numportertotalunir <- numportertotal

      panelunir <- panel


      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))






      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)



      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 17895, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db,17895, length(genes), lower.tail = FALSE))



      triucusin = triucusin[order(triucusin$adjust_pvalue),]
      triucusin = triucusin[,c(1:2,6,3:5,7)]


      qglist <- QueryGenes(genes,database = "MGD", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(3,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

      # Esto es para decidir el número de términos para plotear
      triucu = triucusin[plotnumber,]



    }

    # Si todas
    if (database == "ALL") {

      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/hpodatatotales.csv"), header = T)


      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes,database = "HPO",organism)

      names(panel) = c( "term_id",   "term_name", "gene_overlap")

      # Make total_size_ratio per total
      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal


      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))




      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19248, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19248, length(genes), lower.tail = FALSE))

      triucusin = triucusin[,c(1:2,6,3:5,7)]


      qglist <- QueryGenes(genes,database = "HPO", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(3,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

      # Aqui guardo la fuente de datos de hpo
      triucusinhpo = triucusin


      # Aqui empieza CRB

      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/cbrtotalnumber.csv"), header = T)
      names(numportertotal) = c("term_id",   "term_name", "genes_associated_in_db")

      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes,database = "CRB",organism)

      names(panel) = c( "term_id",   "term_name", "gene_overlap")

      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal



      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))






      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19274, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19274, length(genes), lower.tail = FALSE))


      triucusin = triucusin[,c(1:2,6,3:5,7)]
      qglist <- QueryGenes(genes,database = "CRB", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(4,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

      # Aqui guardo la fuente de datos de hpo
      triucusincrb = triucusin

      # Aqui empieza MGD
      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/totalmponumber.tsv"), header = T, sep = "\t")

      names(numportertotal) <- c("term_id", "term_name", "genes_associated_in_db")

      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes, database = "MGD",organism)



      numportertotalunir <- numportertotal


      panelunir <- panel


      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))





      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)



      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 17895, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 17895, length(genes), lower.tail = FALSE))

      triucusin = triucusin[,c(1:2,6,3:5,7)]

      qglist <- QueryGenes(genes,database = "MGD", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(3,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")


      triucusinmgd = triucusin

      triucusin = rbind(triucusinhpo,triucusincrb,triucusinmgd)


      triucusin = triucusin[order(triucusin$adjust_pvalue),]


      # Esto es para decidir el número de términos para plotear
      triucu = triucusin[plotnumber,]


    }

    if (database == "CRBMGD") {



      # Aqui empieza CRB

      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/cbrtotalnumber.csv"), header = T)
      names(numportertotal) = c("term_id",   "term_name", "genes_associated_in_db")

      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes,database = "CRB",organism)

      names(panel) = c( "term_id",   "term_name", "gene_overlap")

      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal



      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))






      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19274, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19274, length(genes), lower.tail = FALSE))


      triucusin = triucusin[,c(1:2,6,3:5,7)]
      qglist <- QueryGenes(genes,database = "CRB", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(4,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

      # Aqui guardo la fuente de datos de hpo
      triucusincrb = triucusin

      # Aqui empieza MGD
      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/totalmponumber.tsv"), header = T, sep = "\t")

      names(numportertotal) <- c("term_id", "term_name", "genes_associated_in_db")

      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes, database = "MGD",organism)



      numportertotalunir <- numportertotal


      panelunir <- panel


      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))





      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)



      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 17895, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 17895, length(genes), lower.tail = FALSE))

      triucusin = triucusin[,c(1:2,6,3:5,7)]

      qglist <- QueryGenes(genes,database = "MGD", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(3,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")


      triucusinmgd = triucusin

      triucusin = rbind(triucusincrb,triucusinmgd)


      triucusin = triucusin[order(triucusin$adjust_pvalue),]


      # Esto es para decidir el número de términos para plotear
      triucu = triucusin[plotnumber,]


    }

    if (database == "HPOMGD") {

      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/hpodatatotales.csv"), header = T)


      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes,database = "HPO",organism)

      names(panel) = c( "term_id",   "term_name", "gene_overlap")

      # Make total_size_ratio per total
      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal


      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))




      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19248, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19248, length(genes), lower.tail = FALSE))

      triucusin = triucusin[,c(1:2,6,3:5,7)]


      qglist <- QueryGenes(genes,database = "HPO", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(3,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

      # Aqui guardo la fuente de datos de hpo
      triucusinhpo = triucusin

      # Aqui empieza MGD
      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/totalmponumber.tsv"), header = T, sep = "\t")

      names(numportertotal) <- c("term_id", "term_name", "genes_associated_in_db")

      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes, database = "MGD",organism)



      numportertotalunir <- numportertotal


      panelunir <- panel


      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))





      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)



      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 17895, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 17895, length(genes), lower.tail = FALSE))

      triucusin = triucusin[,c(1:2,6,3:5,7)]

      qglist <- QueryGenes(genes,database = "MGD", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(3,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")


      triucusinmgd = triucusin

      triucusin = rbind(triucusinhpo,triucusinmgd)


      triucusin = triucusin[order(triucusin$adjust_pvalue),]


      # Esto es para decidir el número de términos para plotear
      triucu = triucusin[plotnumber,]


    }

    if (database == "HPOCRB") {
      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/hpodatatotales.csv"), header = T)


      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes,database = "HPO",organism)

      names(panel) = c( "term_id",   "term_name", "gene_overlap")

      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal



      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))






      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19248, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19248, length(genes), lower.tail = FALSE))

      triucusin = triucusin[,c(1:2,6,3:5,7)]
      qglist <- QueryGenes(genes,database = "HPO", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(3,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

      # Aqui guardo la fuente de datos de hpo
      triucusinhpo = triucusin


      # Aqui empieza CRB

      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/cbrtotalnumber.csv"), header = T)
      names(numportertotal) = c("term_id",   "term_name", "genes_associated_in_db")


      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes,database = "CRB",organism)

      names(panel) = c( "term_id",   "term_name", "gene_overlap")

      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal



      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))




      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19274, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19274, length(genes), lower.tail = FALSE))

      triucusin = triucusin[,c(1:2,6,3:5,7)]

      qglist <- QueryGenes(genes,database = "CRB", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(3,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_name")

      # Aqui guardo la fuente de datos de hpo
      triucusincrb = triucusin


      triucusin = rbind(triucusinhpo,triucusincrb)


      triucusin = triucusin[order(triucusin$adjust_pvalue),]

      # Esto es para decidir el número de términos para plotear
      triucu = triucusin[plotnumber,]



    }

    # Si todas
    if (database == "HUMANDB") {
      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/hpodatatotales.csv"), header = T)


      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes,database = "HPO",organism)

      names(panel) = c( "term_id",   "term_name", "gene_overlap")

      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal



      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))






      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19248, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19248, length(genes), lower.tail = FALSE))

      triucusin = triucusin[,c(1:2,6,3:5,7)]
      qglist <- QueryGenes(genes,database = "HPO", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(3,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

      # Aqui guardo la fuente de datos de hpo
      triucusinhpo = triucusin


      # Aqui empieza CRB

      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/cbrtotalnumber.csv"), header = T)
      names(numportertotal) = c("term_id",   "term_name", "genes_associated_in_db")


      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes,database = "CRB",organism)

      names(panel) = c( "term_id",   "term_name", "gene_overlap")

      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal



      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))




      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19274, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19274, length(genes), lower.tail = FALSE))

      triucusin = triucusin[,c(1:2,6,3:5,7)]

      qglist <- QueryGenes(genes,database = "CRB", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(3,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_name")

      # Aqui guardo la fuente de datos de hpo
      triucusincrb = triucusin


      triucusin = rbind(triucusinhpo,triucusincrb)


      triucusin = triucusin[order(triucusin$adjust_pvalue),]

      # Esto es para decidir el número de términos para plotear
      triucu = triucusin[plotnumber,]



    }


    p <- triucu %>%

      # prepare text for tooltip
      dplyr::mutate(text = paste("ID Number: ", term_id, "\nTerm: ", term_name,  "\nGene Ratio: ", overlap_ratio, "\nAdjust pvalue: ", adjust_pvalue, "\nCommon genes: ", common_genes)) %>%
      # Classic ggplot
      ggplot(  ggplot2::aes(x= gene_overlap , y= reorder(paste0("",term_id,": ",term_name,""), -adjust_pvalue),  fill = adjust_pvalue, text=text)) +
      theme (text = element_text(size= 8)) +
      geom_bar(stat="identity", alpha=0.7) +
      scale_fill_gradientn(colours = rainbow(2), trans= "log") +
      theme(legend.position="right") +
      labs(title = "Phenotype Enrichment Analysis", x = "Number of genes associated with the term", y = "", fill = "p.value") + #
      theme(plot.title = element_text(size = 15),
            axis.title.x = element_text(size = 10),
            axis.title.y = element_text(size = 10),
            axis.text = element_text(
              angle = 0))

    # turn ggplot interactive with plotly layout.title
    nuevvogg <- ggplotly(p, tooltip="text") %>%
      config(mathjax = "cdn",
             toImageButtonOptions = list(
               format = "svg",
               filename = "myplot",
               width = 1000,
               height = 600
             )
      )



  }

  if (organism == "mouse") {
    genes <- as.data.frame(genes)
    names(genes) <- "mouse_symbol"
    genes$mouse_symbol <- as.character(genes$mouse_symbol)

    # Read database
    homologia <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                              "/homology.csv"), header = T)

    # We will keep only the selected genes
    onlygenes <- dplyr::inner_join(genes, homologia, by = "mouse_symbol")
    onlygenes <- onlygenes$human_symbol
    onlygenes <- as.data.frame(onlygenes)
    names(onlygenes) <- "symbol"
    onlygenes$symbol <- as.character(onlygenes$symbol)
    genes = onlygenes

    # Si solo HPO
    if (database == "HPO") {


      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/hpodatatotales.csv"), header = T)


      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes, database = "HPO", organism)


      # Make total_size_ratio per total
      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal


      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))


      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19248, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19248, length(genes), lower.tail = FALSE))


      triucusin = triucusin[order(triucusin$adjust_pvalue),]

      triucusin = triucusin[,c(1:2,6,3:5,7)]

      qglist <- QueryGenes(genes,database = "HPO", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))



      listadegenesqg <- unique(listadegenesqg[,c(3,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

      # Esto es para decidir el número de términos para plotear
      triucu = triucusin[plotnumber,]


    }

    # Si solo CRB
    if (database == "CRB") {
      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/cbrtotalnumber.csv"), header = T)
      names(numportertotal) = c("term_id",   "term_name", "genes_associated_in_db")


      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes,database = "CRB",organism)

      names(panel) = c( "term_id",   "term_name", "gene_overlap")

      # Make total_size_ratio per total
      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal



      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))




      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)



      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19274, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19274, length(genes), lower.tail = FALSE))


      triucusin = triucusin[order(triucusin$adjust_pvalue),]

      triucusin = triucusin[,c(1:2,6,3:5,7)]

      qglist <- QueryGenes(genes,database = "CRB", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(4,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

      # Esto es para decidir el número de términos para plotear
      triucu = triucusin[plotnumber,]

    }

    # Si solo MGD
    if (database == "MGD"  | database == "MOUSEDB"){

      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/totalmponumber.tsv"), header = T, sep = "\t")


      names(numportertotal) <- c("term_id", "term_name", "genes_associated_in_db")

      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes, database = "MGD",organism)


      # Make total_size_ratio per total
      numportertotalunir <- numportertotal

      panelunir <- panel


      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))






      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)



      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 17895, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db,17895, length(genes), lower.tail = FALSE))



      triucusin = triucusin[order(triucusin$adjust_pvalue),]
      triucusin = triucusin[,c(1:2,6,3:5,7)]


      qglist <- QueryGenes(genes,database = "MGD", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(3,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

      # Esto es para decidir el número de términos para plotear
      triucu = triucusin[plotnumber,]



    }

    # Si todas
    if (database == "ALL") {

      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/hpodatatotales.csv"), header = T)


      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes,database = "HPO",organism)

      names(panel) = c( "term_id",   "term_name", "gene_overlap")

      # Make total_size_ratio per total
      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal


      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))




      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19248, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19248, length(genes), lower.tail = FALSE))

      triucusin = triucusin[,c(1:2,6,3:5,7)]


      qglist <- QueryGenes(genes,database = "HPO", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(3,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

      # Aqui guardo la fuente de datos de hpo
      triucusinhpo = triucusin


      # Aqui empieza CRB

      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/cbrtotalnumber.csv"), header = T)
      names(numportertotal) = c("term_id",   "term_name", "genes_associated_in_db")

      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes,database = "CRB",organism)

      names(panel) = c( "term_id",   "term_name", "gene_overlap")

      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal



      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))






      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19274, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19274, length(genes), lower.tail = FALSE))


      triucusin = triucusin[,c(1:2,6,3:5,7)]
      qglist <- QueryGenes(genes,database = "CRB", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(4,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

      # Aqui guardo la fuente de datos de hpo
      triucusincrb = triucusin

      # Aqui empieza MGD
      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/totalmponumber.tsv"), header = T, sep = "\t")

      names(numportertotal) <- c("term_id", "term_name", "genes_associated_in_db")

      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes, database = "MGD",organism)



      numportertotalunir <- numportertotal


      panelunir <- panel


      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))





      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)



      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 17895, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 17895, length(genes), lower.tail = FALSE))

      triucusin = triucusin[,c(1:2,6,3:5,7)]

      qglist <- QueryGenes(genes,database = "MGD", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(3,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")


      triucusinmgd = triucusin

      triucusin = rbind(triucusinhpo,triucusincrb,triucusinmgd)


      triucusin = triucusin[order(triucusin$adjust_pvalue),]


      # Esto es para decidir el número de términos para plotear
      triucu = triucusin[plotnumber,]


    }

    if (database == "CRBMGD") {



      # Aqui empieza CRB

      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/cbrtotalnumber.csv"), header = T)
      names(numportertotal) = c("term_id",   "term_name", "genes_associated_in_db")

      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes,database = "CRB",organism)

      names(panel) = c( "term_id",   "term_name", "gene_overlap")

      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal



      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))






      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19274, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19274, length(genes), lower.tail = FALSE))


      triucusin = triucusin[,c(1:2,6,3:5,7)]
      qglist <- QueryGenes(genes,database = "CRB", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(4,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

      # Aqui guardo la fuente de datos de hpo
      triucusincrb = triucusin

      # Aqui empieza MGD
      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/totalmponumber.tsv"), header = T, sep = "\t")

      names(numportertotal) <- c("term_id", "term_name", "genes_associated_in_db")

      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes, database = "MGD",organism)



      numportertotalunir <- numportertotal


      panelunir <- panel


      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))





      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)



      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 17895, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 17895, length(genes), lower.tail = FALSE))

      triucusin = triucusin[,c(1:2,6,3:5,7)]

      qglist <- QueryGenes(genes,database = "MGD", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(3,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")


      triucusinmgd = triucusin

      triucusin = rbind(triucusincrb,triucusinmgd)


      triucusin = triucusin[order(triucusin$adjust_pvalue),]


      # Esto es para decidir el número de términos para plotear
      triucu = triucusin[plotnumber,]


    }

    if (database == "HPOMGD") {

      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/hpodatatotales.csv"), header = T)


      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes,database = "HPO",organism)

      names(panel) = c( "term_id",   "term_name", "gene_overlap")

      # Make total_size_ratio per total
      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal


      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))




      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19248, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19248, length(genes), lower.tail = FALSE))

      triucusin = triucusin[,c(1:2,6,3:5,7)]


      qglist <- QueryGenes(genes,database = "HPO", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(3,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

      # Aqui guardo la fuente de datos de hpo
      triucusinhpo = triucusin

      # Aqui empieza MGD
      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/totalmponumber.tsv"), header = T, sep = "\t")

      names(numportertotal) <- c("term_id", "term_name", "genes_associated_in_db")

      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes, database = "MGD",organism)



      numportertotalunir <- numportertotal


      panelunir <- panel


      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))





      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)



      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 17895, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 17895, length(genes), lower.tail = FALSE))

      triucusin = triucusin[,c(1:2,6,3:5,7)]

      qglist <- QueryGenes(genes,database = "MGD", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(3,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")


      triucusinmgd = triucusin

      triucusin = rbind(triucusinhpo,triucusinmgd)


      triucusin = triucusin[order(triucusin$adjust_pvalue),]


      # Esto es para decidir el número de términos para plotear
      triucu = triucusin[plotnumber,]


    }

    # Si todas
    # Si todas
    if (database == "HUMANDB") {
      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/hpodatatotales.csv"), header = T)


      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes,database = "HPO",organism)

      names(panel) = c( "term_id",   "term_name", "gene_overlap")

      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal



      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))






      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19248, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19248, length(genes), lower.tail = FALSE))

      triucusin = triucusin[,c(1:2,6,3:5,7)]
      qglist <- QueryGenes(genes,database = "HPO", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(3,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

      # Aqui guardo la fuente de datos de hpo
      triucusinhpo = triucusin


      # Aqui empieza CRB

      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/cbrtotalnumber.csv"), header = T)
      names(numportertotal) = c("term_id",   "term_name", "genes_associated_in_db")


      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumber(genes,database = "CRB",organism)

      names(panel) = c( "term_id",   "term_name", "gene_overlap")

      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal



      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))




      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19274, length(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19274, length(genes), lower.tail = FALSE))

      triucusin = triucusin[,c(1:2,6,3:5,7)]

      qglist <- QueryGenes(genes,database = "CRB", organism )
      urls <- paste0("https://www.genecards.org/cgi-bin/carddisp.pl?gene=",qglist$symbol,"")
      qglist$symbol <- paste0("[", qglist$symbol, "](", urls, ")")

      listadegenesqg <- qglist %>%
        group_by(term_id) %>%
        mutate(common_genes = paste0(symbol, collapse = ", "))


      listadegenesqg <- unique(listadegenesqg[,c(3,5)])

      triucusin <- inner_join(triucusin,listadegenesqg, by = "term_name")

      # Aqui guardo la fuente de datos de hpo
      triucusincrb = triucusin


      triucusin = rbind(triucusinhpo,triucusincrb)


      triucusin = triucusin[order(triucusin$adjust_pvalue),]

      # Esto es para decidir el número de términos para plotear
      triucu = triucusin[plotnumber,]



    }


    p <- triucu %>%

      # prepare text for tooltip
      dplyr::mutate(text = paste("ID Number: ", term_id, "\nTerm: ", term_name,  "\nGene Ratio: ", overlap_ratio, "\nAdjust pvalue: ", adjust_pvalue, "\nCommon genes: ", common_genes)) %>%
      # Classic ggplot
      ggplot(  ggplot2::aes(x= gene_overlap , y= reorder(paste0("",term_id,": ",term_name,""), -adjust_pvalue),  fill = adjust_pvalue, text=text)) +
      theme (text = element_text(size= 8)) +
      geom_bar(stat="identity", alpha=0.7) +
      scale_fill_gradientn(colours = rainbow(2), trans= "log") +
      theme(legend.position="right") +
      labs(title = "Phenotype Enrichment Analysis", x = "Number of genes associated with the term", y = "", fill = "p.value") + #
      theme(plot.title = element_text(size = 15),
            axis.title.x = element_text(size = 10),
            axis.title.y = element_text(size = 10),
            axis.text = element_text(
              angle = 0))

    # turn ggplot interactive with plotly layout.title
    nuevvogg <- ggplotly(p, tooltip="text") %>%
      config(mathjax = "cdn",
             toImageButtonOptions = list(
               format = "svg",
               filename = "myplot",
               width = 1000,
               height = 600
             )
      )



  }

  newList <- list("alldata" = triucusin,   "graph" = nuevvogg)
  return(newList)
}



#' @title PhenoEnrichCompareRandom
#' @description This function makes a plot with enriched phenotype info.
#' @param genes A vector with human gene symbol.
#' @param database A string paramater with different options. You can define a vector with the databases.
#' @param organism A string paramater: "human" if you have human genes and "mouse" if you have mouse genes
#' @param plotn An integer with the number of term that you want in the plot. The default value is 30.
#' @details This function has developed by Alejandro Cisterna García as part of his PhD mentored by Juan Antonio Botía Blaya
#' @export



PhenoEnrichCompareRandom = function(onlygenes, geneset, genesetcompare, database = "HPO", organism = "human", plotn=30, plotnumber = 1:plotn){


  if (organism == "human") {



    # Make two empty df with names
    rbjointri = data.frame(matrix(vector(), 0, 9,
                                  dimnames=list(c(), c("term_id", "term_name", "source", "source", "adjust_pvalue",
                                                       "genes_associated_in_db","gene_overlap", "overlap_ratio"
                                                       ,"pvalue"))),
                           stringsAsFactors=F)

    # For each database name in a vector run its loadder
    for (db in database) {

      # Get the loadder name
      getnamedb <- paste0("",db,"loaddercompareOpt")

      # Run the loadder
      peaload <- get(getnamedb)(onlygenes, geneset, genesetcompare,organism = "human", plotnumber = plotn)

      # join data frames generated
      rbjointri <- rbind(rbjointri, peaload)

      # Order by pvalue
      rbjointri = rbjointri[order(rbjointri$adjust_pvalue),] # alldata

      # To plot
      triucu = rbjointri[1:plotn,]
    }
  }



  if (organism == "mouse") {
    genes <- as.data.frame(genesetcompare)
    names(genes) <- "mouse_symbol"
    genes$mouse_symbol <- as.character(genes$mouse_symbol)

    # Read database
    homologia <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                              "/homology.csv"), header = T)

    # We will keep only the selected genes
    onlygenes <- dplyr::inner_join(genes, homologia, by = "mouse_symbol")
    onlygenes <- onlygenes$human_symbol
    onlygenes <- as.data.frame(onlygenes)
    names(onlygenes) <- "symbol"
    onlygenes$symbol <- as.character(onlygenes$symbol)
    genesetcompare = unique(onlygenes)

    genesr <- as.data.frame(geneset)
    names(genesr) <- "mouse_symbol"
    genesr$mouse_symbol <- as.character(genesr$mouse_symbol)

    # We will keep only the selected genes
    onlygenes <- dplyr::inner_join(genesr, homologia, by = "mouse_symbol")
    onlygenes <- onlygenes$human_symbol
    onlygenes <- as.data.frame(onlygenes)
    names(onlygenes) <- "symbol"
    onlygenes$symbol <- as.character(onlygenes$symbol)
    genesr = unique(onlygenes)


    # Encontrar el no overlap entre unos y otros
    onlygenes <- foundgeneoverlap(genesr, genesetcompare)


    # Make two empty df with names
    rbjointri = data.frame(matrix(vector(), 0, 8,
                                  dimnames=list(c(), c("term_id", "term_name", "source", "adjust_pvalue",
                                                       "genes_associated_in_db","gene_overlap", "overlap_ratio"
                                                       ,"pvalue"))),
                           stringsAsFactors=F)

    # For each database name in a vector run its loadder
    for (db in database) {

      # Get the loadder name
      getnamedb <- paste0("",db,"loaddercompareOpt")

      # Run the loadder
      peaload <- get(getnamedb)(onlygenes, genesetcompare,organism = "human", plotnumber = plotn)

      # join data frames generated
      rbjointri <- rbind(rbjointri, peaload)

      # Order by pvalue
      rbjointri = rbjointri[order(rbjointri$adjust_pvalue),] # alldata

      # To plot
      triucu = rbjointri[1:plotn,]
    }
  }

  newList <- rbjointri
  return(newList)
}



#' @title foundgeneoverlap
#' @description This function give no overlapping genes
#' @param geneset A vector with human gene symbol.
#' @param genesetcompare A vector with human gene symbol.
#' @details This function has developed by Alejandro Cisterna García as part of his PhD mentored by Juan Antonio Botía Blaya
#' @export




# Función para obtener el enriquecimiento de términos HPO Y CRB
foundgeneoverlap = function(geneset, genesetcompare){


    # Ver cuantos genes no hacen overlap para sacarlos del cálculo del target
    # Covert vector to a daframe
    referencegenesc <- as.data.frame(geneset)
    names(referencegenesc) <- "Symbol"

    # Pasar estos genes a entrezid clusterprofiler
    genes.df <- bitr(referencegenesc$Symbol, fromType = "SYMBOL",
                     toType = c("SYMBOL", "ENTREZID"),
                     OrgDb = "org.Hs.eg.db")
    referencegenesc <- unique(genes.df)
    names(referencegenesc) <- c("symbol","entrez")
    referencegenesc$entrez <- as.numeric(as.character(referencegenesc$entrez))


    targetgenesc <- as.data.frame(genesetcompare)
    names(targetgenesc) <- "Symbol"

    # Pasar estos genes a entrezid clusterprofiler
    genes.df <- bitr(targetgenesc$Symbol, fromType = "SYMBOL",
                     toType = c("SYMBOL", "ENTREZID"),
                     OrgDb = "org.Hs.eg.db")
    targetgenesc <- unique(genes.df)
    names(targetgenesc) <- c("symbol","entrez")
    targetgenesc$entrez <- as.numeric(as.character(targetgenesc$entrez))



    # We will keep only the selected genes
    onlygenes <- dplyr::anti_join(referencegenesc, targetgenesc, by = "entrez")




  return(onlygenes)
}





#



#' @title PhenoRelation
#' @description This function give no overlapping genes
#' @param genes A vector with human gene symbol.
#' @details This function has developed by Alejandro Cisterna García as part of his PhD mentored by Juan Antonio Botía Blaya
#' @export

PhenoRelation = function(enrichresult, genes,  p.value = 0.05){


  # Tomar los fenotipos estadisticamente significativos
  relevantes <- dplyr::filter(enrichresult, enrichresult$adjust_pvalue <= p.value)


    p <- relevantes %>%  dplyr::mutate(db = case_when(stringr::str_detect(relevantes$term_id, "HP")  ~ "HPO",
                                                      stringr::str_detect(relevantes$term_id, "MP") ~ "MPO",
                                                      stringr::str_detect(relevantes$term_id, "CXXX") ~ "DIS",
                                                      stringr::str_detect(relevantes$term_id, "C\\d+") ~ "DIS",
                                                      stringr::str_detect(relevantes$term_id, "CRB")  ~ "CRB"))
    hpo <- dplyr::filter( p,  p$db == "HPO")
    hponr <- nrow(hpo)
    nhpo <-round(hponr * 0.1)
    hpo <- hpo[c(1:nhpo),]


    dis <- dplyr::filter( p,  p$db == "DIS")
    disnr <- nrow(dis)
    ndis <-round(disnr * 0.2)
    dis <- dis[c(1:ndis),]

    hpodis <- rbind(hpo,dis)
    hpodisnomb <- hpodis$term_id


  # Contar los fenotipos significativos
  numerpheno <- nrow(relevantes) # relevantes

  # Obtener el término de todos ellos
  nombresmatriz <- relevantes$term_id
  nombresfenotipos <- as.data.frame(nombresmatriz)
  names(nombresfenotipos) <- "term_id"

  # Crear una matriz con el número de fenotipos relevantes
  matpheno = matrix(data=NA, nrow= numerpheno, ncol=numerpheno)
  rownames(matpheno) <- nombresmatriz
  colnames(matpheno) <- nombresmatriz

  # Aqui ya empieza a depender de las bases de datos que estén marcadas, creo que esto puede ser siempre todas
  allquerygenes <- QueryGenes(genes, database = "ALL")


  # Mis genes
  genes <- as.data.frame(genes)
  names(genes) <- "Symbol"

  # Pasar estos genes a entrezid clusterprofiler
  genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                   toType = c("SYMBOL", "ENTREZID"),
                   OrgDb = "org.Hs.eg.db")
  genes <- unique(genes.df$ENTREZID)
  genes <- as.data.frame(genes)
  names(genes) <- "entrez"
  genes$entrez <- as.numeric(as.character(genes$entrez))

  listadedf = list()
  for (i in 1:numerpheno) {

    # Leo el fenotipo
    phenotipo <- nombresmatriz[i]

    # Me quedo con todos los genes y fenotipos relacionados con esos de mis genes
    genesdeesefenotipo <- dplyr::filter(allquerygenes, allquerygenes$term_id == phenotipo)
    genesdeesefenotipo <- genesdeesefenotipo$entrez

    # De alguna forma parece que pierdo un gen del total?
    # Con estos genes ahora tengo que calcular el PhenoGeneNumber
    numerosdefenotiposhpo <- PhenoGeneNumberOpt(genesdeesefenotipo, database = "HPO")
    numerosdefenotiposmdg <- PhenoGeneNumberOpt(genesdeesefenotipo, database = "MGD")
    numerosdefenotiposdis <- PhenoGeneNumberOpt(genesdeesefenotipo, database = "DIS")
    numerosdefenotiposcrb <- PhenoGeneNumberOpt(genesdeesefenotipo, database = "CRB")

    # Junto los cálculos
    numerosdefenotipos <- rbind(numerosdefenotiposhpo,numerosdefenotiposdis,numerosdefenotiposmdg,numerosdefenotiposcrb)

    names(nombresfenotipos) <- "term_id"
    numerorelevantes <- inner_join(numerosdefenotipos,nombresfenotipos, by = "term_id")


    nopresentes <- anti_join(nombresfenotipos,numerorelevantes, by = "term_id")

    if (nrow(nopresentes)>0) {
      nopresentes$gen_associated_number <- 0
      nopresentes$term_id <- as.character(nopresentes$term_id)
      nopresentes$gen_associated_number <- as.integer(nopresentes$gen_associated_number)
      colnames(nopresentes) <- c("term_id","gene_overlap")
      numerorelevantes = numerorelevantes[,c(1,3)]

      resultadofenotipo <- rbind.data.frame(numerorelevantes,nopresentes)
      names(resultadofenotipo) <- c("term_id", paste0("",phenotipo,"") )

    }else{
      resultadofenotipo =numerorelevantes[,c(1,3)]
      names(resultadofenotipo) <- c("term_id", paste0("",phenotipo,"") )
    }



    listadedf[[i]] <-resultadofenotipo
  }

# Esto se lo come rapido
a <- Reduce(function(...) merge(..., by = "term_id", all=TRUE), listadedf)


  # Hacer que el data frame sea una matriz (Guardarla para enseñar porque tarda)
  mat1 = data.matrix(a)
  rownames(mat1) <-a$term_id
  mat1<-mat1[,-1]

  # Select mp overexpressed
  # select crb overexpressed

  matnew <-mat1[,colnames(mat1) %in% hpodisnomb]



  # El heatmap ya hace un cluster, podemos sacar ese cluster
  # COn el cluster tenemos que ver cuando caen cerca términos de diferentes bases de datos y mostrar eso
  # Tenemos colnames y rownames
  correlacion <- cor(mat1)

  matnew <-correlacion[rownames(correlacion) %in% hpodisnomb,colnames(correlacion) %in% hpodisnomb]

  flattenCorrMatrix <- function(cormat, pmat) {
    ut <- upper.tri(cormat)
    data.frame(
      row = rownames(cormat)[row(cormat)[ut]],
      column = rownames(cormat)[col(cormat)[ut]],
      cor  =(cormat)[ut],
      p = pmat[ut]
    )
  }

  res2<-rcorr(mat1)
  fmatda <- flattenCorrMatrix(res2$r, res2$P)

  heatmapfin <- pheatmap(matnew, cutree_rows = 4)

  newList <- list("cormatrix" = correlacion, "colum" = fmatda, "heatmap" = heatmapfin)
  return(newList)

}

#' @title PhenoGeneNumberSimuok
#' @description This function counts the number of genes per phenotype term from the vector of genes.
#' @param database A string paramater with different options: "ALL", "HUMANDB", "MOUSEDB", "HPO", "CRB" and "MGD"
#' @param genes A vector with human gene symbol.
#' @param organism A string paramater: "human" if you have human genes and "mouse" if you have mouse genes
#' @details This function has developed by Alejandro Cisterna García as part of his PhD mentored by Juan Antonio Botía Blaya
#' @export


PhenoGeneNumberSimuok = function(genes, database= "HPO", organism = "human"){

  if (organism == "human") {

    if (database == "HPO") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/hpobasededatos.csv"), header = T)
      names(custom) <- c("entrez","symbol", "term_id", "term_name")

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "symbol")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "DIS") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"


      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/disbasededatos.csv"), header = T)
      custom <- custom[,c(1,2,5,6)]
      names(custom) <- c("entrez","symbol", "term_id", "term_name")

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "symbol")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")


    }

    if (database == "CRB") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "symbol"





      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/cbrdatabaseok.csv"), header = T)
      names(custom) <- c("entrez","symbol", "term_name", "term_id")

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "symbol")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")
    }

    if (database == "MGD") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "human_symbol"
      genes$HumanSymbol <- as.character(genes$human_symbol)



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/mgibasefinal.tsv"), header = T, sep = "\t")
      names(custom) <- c("entrez","human_symbol", "mouse_symbol", "mgi", "term_id", "term_name", "info"   )

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "human_symbol")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name ) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")

    }
  }

  if (organism == "mouse") {

    # genes = musedata[1:50,3]

    genes <- as.data.frame(genes)
    names(genes) <- "mouse_symbol"
    genes$mouse_symbol <- as.character(genes$mouse_symbol)

    # Read database
    homologia <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                              "/homology.csv"), header = T)

    # We will keep only the selected genes
    onlygenes <- dplyr::inner_join(genes, homologia, by = "mouse_symbol")
    onlygenes <- onlygenes$human_symbol
    onlygenes <- as.data.frame(onlygenes)
    names(onlygenes) <- "symbol"
    onlygenes$symbol <- as.character(onlygenes$symbol)
    genes = onlygenes


    if (database == "HPO") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/hpobasededatos.csv"), header = T)
      names(custom) <- c("entrez","symbol", "term_id", "term_name")

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")
    }

    if (database == "CRB") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "Symbol"

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$Symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))



      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/cbrdatabaseok.csv"), header = T)
      names(custom) <- c("entrez","symbol", "term_name", "term_id")

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")
    }

    if (database == "MGD") {
      # Covert vector to a daframe
      genes <- as.data.frame(genes)
      names(genes) <- "human_symbol"
      genes$HumanSymbol <- as.character(genes$human_symbol)

      # Pasar estos genes a entrezid clusterprofiler
      genes.df <- bitr(genes$human_symbol, fromType = "SYMBOL",
                       toType = c("SYMBOL", "ENTREZID"),
                       OrgDb = "org.Hs.eg.db")
      genes <- unique(genes.df$ENTREZID)
      genes <- as.data.frame(genes)
      names(genes) <- "entrez"
      genes$entrez <- as.numeric(as.character(genes$entrez))

      # Read database
      custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                             "/mgibasefinal.tsv"), header = T, sep = "\t")
      names(custom) <- c("entrez","human_symbol", "mouse_symbol", "mgi", "term_id", "term_name", "info"   )

      # We will keep only the selected genes
      onlygenes <- dplyr::inner_join(custom, genes, by = "entrez")

      # We will count the number of genes per phenotype
      numportertotal <-   onlygenes %>% dplyr::group_by(term_id, term_name ) %>% dplyr::tally()
      numportergenes <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
      names(numportergenes) <- c("term_id", "term_name", "gene_overlap")

    }
  }

  return(numportergenes)
}

#' @title PhenoEnrichGenespransimu
#' @description This function analyses the similarity of phenotypic terms between two groups of genes
#' @param genes A vector with gene symbol, fixed genes or panel genes.
#' @param organism A string paramater: "human" if you have human genes and "mouse" if you have mouse genes
#' @param database A string paramater with different options: "ALL", "HUMANDB", "MOUSEDB", "HPO", "CRB" and "MGD"
#' @param p.value Cut of p value as they are relevant terms
#' @param nulltestnumber A number of random genes subset.
#' @param seed A seed.
#' @details This function has developed by Alejandro Cisterna García as part of his PhD mentored by Juan Antonio Botía Blaya
#' @export


PhenoEnrichGenespransimu = function(genes, database = "HPO", organism = "human"){


  if (organism == "human") {

    # Si solo HPO
    if (database == "HPO") {

      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/hpodatatotales.csv"), header = T)


      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumberSimuok(genes,database = "HPO",organism)

      names(panel) = c( "term_id",   "term_name", "gene_overlap")

      # Make total_size_ratio per total
      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal


      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))




      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19248, nrow(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19248, nrow(genes), lower.tail = FALSE))

      triucusin = triucusin[,c(1:2,6,3:5,7)]


    }

    # Si solo CRB
    if (database == "CRB") {
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/cbrtotalnumber.csv"), header = T)
      names(numportertotal) = c("term_id",   "term_name", "genes_associated_in_db")

      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumberSimuok(genes,database = "CRB",organism)

      names(panel) = c( "term_id",   "term_name", "gene_overlap")

      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal



      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))






      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19274, nrow(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19274, nrow(genes), lower.tail = FALSE))


      triucusin = triucusin[,c(1:2,6,3:5,7)]


    }

    # Si solo MGD
    if (database == "MGD"  | database == "MOUSEDB"){

      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/totalmponumber.tsv"), header = T, sep = "\t")

      names(numportertotal) <- c("term_id", "term_name", "genes_associated_in_db")

      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumberSimuok(genes, database = "MGD",organism)



      numportertotalunir <- numportertotal


      panelunir <- panel


      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))





      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)



      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 17895, nrow(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 17895, nrow(genes), lower.tail = FALSE))

      triucusin = triucusin[,c(1:2,6,3:5,7)]


    }

    if (database == "DIS") {

      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/disdatostotales.csv"), header = T)


      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumberSimuok(genes, database = "DIS", organism)


      # Make total_size_ratio per total
      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal


      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))


      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19722, nrow(genes), lower.tail = FALSE), method = "bonferroni")) %>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19722, nrow(genes), lower.tail = FALSE))


      triucusin = triucusin[order(triucusin$adjust_pvalue),]

      triucusin = triucusin[,c(1:2,6,3:5,7)]

    }

    # Si todas
    if (database == "ALL") {

      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/hpodatatotales.csv"), header = T)


      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumberSimuok(genes,database = "HPO",organism)

      names(panel) = c( "term_id",   "term_name", "gene_overlap")

      # Make total_size_ratio per total
      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal


      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))




      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19248, nrow(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19248, nrow(genes), lower.tail = FALSE))

      triucusin = triucusin[,c(1:2,6,3:5,7)]


      # Aqui guardo la fuente de datos de hpo
      triucusinhpo = triucusin


      # Aqui empieza CRB

      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/cbrtotalnumber.csv"), header = T)
      names(numportertotal) = c("term_id",   "term_name", "genes_associated_in_db")

      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumberSimuok(genes,database = "CRB",organism)

      names(panel) = c( "term_id",   "term_name", "gene_overlap")

      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal



      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))






      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19274, nrow(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19274, nrow(genes), lower.tail = FALSE))


      triucusin = triucusin[,c(1:2,6,3:5,7)]

      # Aqui guardo la fuente de datos de hpo
      triucusincrb = triucusin

      # Aqui empieza MGD
      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/totalmponumber.tsv"), header = T, sep = "\t")

      names(numportertotal) <- c("term_id", "term_name", "genes_associated_in_db")

      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumberSimuok(genes, database = "MGD",organism)



      numportertotalunir <- numportertotal


      panelunir <- panel


      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))





      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)



      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 17895, nrow(genes), lower.tail = FALSE), method = "bonferroni"))%>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 17895, nrow(genes), lower.tail = FALSE))

      triucusin = triucusin[,c(1:2,6,3:5,7)]
      triucusinmgd = triucusin


      # Read data base
      numportertotal <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/disdatostotales.csv"), header = T)


      # We will count the number of genes per phenotype
      panel <- PhenoGeneNumberSimuok(genes, database = "DIS", organism)


      # Make total_size_ratio per total
      #numportertotalunir <- numportertotal[,c(1,2,4)]
      numportertotalunir <- numportertotal


      #panelunir <- panel[,c(1,2,4)]
      panelunir <- panel

      names(numportertotalunir) = c( "term_id",   "term_name", "genes_associated_in_db")

      triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))


      # Esto es para que almenos tenga anotados 10 genes en ese fenotipo
      triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)


      triucusin <- triucu

      # Add gene total_size_ratio col
      triucusin <- triucusin %>%
        dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


      # Calculate p.value
      triucusin <- triucusin %>%
        dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19722, nrow(genes), lower.tail = FALSE), method = "bonferroni")) %>%
        dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, 19722, nrow(genes), lower.tail = FALSE))

      triucusin = triucusin[order(triucusin$adjust_pvalue),]

      triucusin = triucusin[,c(1:2,6,3:5,7)]



      triucusindis = triucusin



      triucusin = rbind(triucusinhpo,triucusincrb,triucusinmgd,triucusindis)


      triucusin = triucusin[order(triucusin$adjust_pvalue),]




    }


  }

  return(triucusin)

}



#' @title HPOloadder
#' @export


HPOloadder = function(genes, organism = "human", plotnumber = 30, url = F){

  # Read data base
  numportertotalunir <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/hpodatatotales.csv"), header = T)


  # We will count the number of genes per phenotype
  panelunir <- PhenoGeneNumber(genes, database = "HPO", organism)

  # We join the dataframes
  triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

  # Save phenotypes with at least 10 genes
  triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)

  # To have two DF with different names
  triucusin <- triucu

  # Add gene overlap_ratio
  triucusin <- triucusin %>%
    dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


  # Calculate p.value (genes in each database)
  triucusin <- triucusin %>%
    dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19248 - genes_associated_in_db, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
    dplyr::mutate(pvalue = phyper(gene_overlap -1, genes_associated_in_db, 19248 - genes_associated_in_db, length(genes), lower.tail = FALSE))


  # Order by pvalue
  triucusin = triucusin[order(triucusin$adjust_pvalue),]
  triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

  # Get each gene per phenotype
  qglist <- QueryGenes(genes,database = "HPO", organism )

  if (url ) {

    qglist$symbol <- paste0("<a href='https://www.genecards.org/cgi-bin/carddisp.pl?gene=", qglist$symbol,"'>", qglist$symbol,"</a>")

  }
  # Get the url for gene

  # Collapse genes per phenotype
  listadegenesqg <- qglist %>%
    group_by(term_id) %>%
    mutate(common_genes = paste0(symbol, collapse = ", "))

  # Unique names
  listadegenesqg <- unique(listadegenesqg[,c(3,6)])

  #listadegenesqg[,c(3,6)]

  # Join genes and dataframe
  triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

  # Esto es para decidir el número de términos para plotear
  triucu = triucusin[1:plotnumber,]


  # Make interactive table with phenotype url
  htmlttriucu <- triucusin
  if (url ) {
  htmlttriucu$term_id <- paste0("<a href='https://hpo.jax.org/app/browse/term/", htmlttriucu$term_id,"'>", htmlttriucu$term_id,"</a>")
  }

  # Return list
  newList <- list("alldata" = triucusin, "htmltabla" = htmlttriucu)
  return(newList)
}


#' @title MGDloadder
#' @export

MGDloadder = function(genes, organism = "human", plotnumber = 30, url = F){
  # Read data base
  numportertotalunir <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/totalmponumber.tsv"), header = T, sep = "\t")

  # We will count the number of genes per phenotype
  panelunir <- PhenoGeneNumber(genes, database = "MGD", organism)

  # We join the dataframes
  triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

  # Save phenotypes with at least 10 genes
  triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)

  # To have two DF with different names
  triucusin <- triucu

  # Add gene overlap_ratio
  triucusin <- triucusin %>%
    dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


  # Calculate p.value (genes in each database)
  triucusin <- triucusin %>%
    dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 17895 - genes_associated_in_db, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
    dplyr::mutate(pvalue = phyper(gene_overlap -1, genes_associated_in_db, 17895 - genes_associated_in_db, length(genes), lower.tail = FALSE))


  # Order by pvalue
  triucusin = triucusin[order(triucusin$adjust_pvalue),]
  triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

  # Get each gene per phenotype
  qglist <- QueryGenes(genes,database = "MGD", organism )
  if (url) {
  # Get the url for gene
  qglist$symbol <- paste0("<a href='https://www.genecards.org/cgi-bin/carddisp.pl?gene=", qglist$symbol,"'>", qglist$symbol,"</a>")
  }

  # Collapse genes per phenotype
  listadegenesqg <- qglist %>%
    group_by(term_id) %>%
    mutate(common_genes = paste0(symbol, collapse = ", "))

  # Unique
  listadegenesqg <- unique(listadegenesqg[,c(3,5)])

  # Join genes and dataframe
  triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

  # Esto es para decidir el número de términos para plotear
  triucu = triucusin[1:plotnumber,]


  # Make interactive table with phenotype url
  htmlttriucu <- triucusin
  if (url) {
  htmlttriucu$term_id <- paste0("<a href='http://www.informatics.jax.org/vocab/mp_ontology/", htmlttriucu$term_id,"'>", htmlttriucu$term_id,"</a>")
}
  newList <- list("alldata" = triucusin, "htmltabla" = htmlttriucu)
  return(newList)
}


#' @title CRBloadder
#' @export
CRBloadder = function(genes, organism = "human", plotnumber = 30, url = F){
  # Read data base
  numportertotalunir <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/cbrtotalnumber.csv"), header = T)

  # We will count the number of genes per phenotype
  panelunir <- PhenoGeneNumber(genes, database = "CRB", organism)

  # We join the dataframes
  triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

  # Save phenotypes with at least 10 genes
  triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)

  # To have two DF with different names
  triucusin <- triucu

  # Add gene overlap_ratio
  triucusin <- triucusin %>%
    dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


  # Calculate p.value (genes in each database)
  triucusin <- triucusin %>%
    dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19274 - genes_associated_in_db, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
    dplyr::mutate(pvalue = phyper(gene_overlap -1, genes_associated_in_db, 19274  - genes_associated_in_db , length(genes), lower.tail = FALSE))


  # Order by pvalue
  triucusin = triucusin[order(triucusin$adjust_pvalue),]
  triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

  # Get each gene per phenotype
  qglist <- QueryGenes(genes,database = "CRB", organism )

  if (url) {
  # Get the url for gene
  qglist$symbol <- paste0("<a href='https://www.genecards.org/cgi-bin/carddisp.pl?gene=", qglist$symbol,"'>", qglist$symbol,"</a>")

  }
  # Collapse genes per phenotype
  listadegenesqg <- qglist %>%
    group_by(term_id) %>%
    mutate(common_genes = paste0(symbol, collapse = ", "))

  # Unique
  listadegenesqg <- unique(listadegenesqg[,c(4,6)])

  # Join genes and dataframe
  triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

  # Esto es para decidir el número de términos para plotear
  triucu = triucusin[1:plotnumber,]


  # Make interactive table with phenotype url
  htmlttriucu <- triucusin
  if (url) {
  htmlttriucu$term_id <- paste0("<a href='https://crisprbrain.org/simple-screen/", htmlttriucu$term_id,"'>", htmlttriucu$term_id,"</a>")
  }

  newList <- list("alldata" = triucusin, "htmltabla" = htmlttriucu)
  return(newList)
}


#' @title CLINGENloadder
#' @export
CLINGENloadder = function(genes, organism = "human", plotnumber = 30,  url = F){

  # Read data base
  numportertotalunir <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/clingendatostotales.csv"), header = T)


  # We will count the number of genes per phenotype
  panelunir <- PhenoGeneNumber(genes, database = "CLINGEN", organism)

  # We join the dataframes
  triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

  # Save diseases terms with at least 5 genes
  triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

  # To have two DF with different names
  triucusin <- triucu

  # Add gene overlap_ratio
  triucusin <- triucusin %>%
    dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


  # Calculate p.value (genes in each database)
  triucusin <- triucusin %>%
    dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19198  - genes_associated_in_db, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
    dplyr::mutate(pvalue = phyper(gene_overlap -1, genes_associated_in_db, 19198 - genes_associated_in_db, length(genes), lower.tail = FALSE))


  # Order by pvalue
  triucusin = triucusin[order(triucusin$adjust_pvalue),]
  triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

  # Get each gene per phenotype
  qglist <- QueryGenes(genes,database = "CLINGEN", organism )

  if (url) {
  # Get the url for gene
  qglist$symbol <- paste0("<a href='https://www.genecards.org/cgi-bin/carddisp.pl?gene=", qglist$symbol,"'>", qglist$symbol,"</a>")
}

  # Collapse genes per phenotype
  listadegenesqg <- qglist %>%
    group_by(term_id) %>%
    mutate(common_genes = paste0(symbol, collapse = ", "))

  # Unique
  listadegenesqg <- unique(listadegenesqg[,c(3,6)])

  # Join genes and dataframe
  triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

  # Esto es para decidir el número de términos para plotear
  triucu = triucusin[1:plotnumber,]


  # Make interactive table with phenotype url
  htmlttriucu <- triucusin
  if (url) {
   htmlttriucu$term_id <- paste0("<a href='https://www.ncbi.nlm.nih.gov/medgen/", htmlttriucu$term_id,"'>", htmlttriucu$term_id,"</a>")
  }

  newList <- list("alldata" = triucusin, "htmltabla" = htmlttriucu)
  return(newList)
}


#' @title GENOMICS_ENGLANDloadder
#' @export
GENOMICS_ENGLANDloadder = function(genes, organism = "human", plotnumber = 30,  url = F){

  # Read data base
  numportertotalunir <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/GENOMICS_ENGLANDdatostotales.csv"), header = T)

  # We will count the number of genes per phenotype
  panelunir <- PhenoGeneNumber(genes, database = "GENOMICS_ENGLAND", organism)

  # We join the dataframes
  triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

  # Save diseases terms with at least 5 genes
  triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

  # To have two DF with different names
  triucusin <- triucu

  # Add gene overlap_ratio
  triucusin <- triucusin %>%
    dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


  # Calculate p.value (genes in each database)
  triucusin <- triucusin %>%
    dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19230 - genes_associated_in_db, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
    dplyr::mutate(pvalue = phyper(gene_overlap -1, genes_associated_in_db, 19230 - genes_associated_in_db, length(genes), lower.tail = FALSE))


  # Order by pvalue
  triucusin = triucusin[order(triucusin$adjust_pvalue),]
  triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

  # Get each gene per phenotype
  qglist <- QueryGenes(genes,database = "GENOMICS_ENGLAND", organism )

  if (url) {
  # Get the url for gene
  qglist$symbol <- paste0("<a href='https://www.genecards.org/cgi-bin/carddisp.pl?gene=", qglist$symbol,"'>", qglist$symbol,"</a>")
}
  # Collapse genes per phenotype
  listadegenesqg <- qglist %>%
    group_by(term_id) %>%
    mutate(common_genes = paste0(symbol, collapse = ", "))

  # Unique
  listadegenesqg <- unique(listadegenesqg[,c(3,6)])

  # Join genes and dataframe
  triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

  # Esto es para decidir el número de términos para plotear
  triucu = triucusin[1:plotnumber,]


  # Make interactive table with phenotype url

   htmlttriucu <- triucusin
   if (url) {
   htmlttriucu$term_id <- paste0("<a href='https://www.ncbi.nlm.nih.gov/medgen/", htmlttriucu$term_id,"'>", htmlttriucu$term_id,"</a>")
}
  newList <- list("alldata" = triucusin, "htmltabla" = htmlttriucu)
  return(newList)
}

#' @title CTDloadder
#' @export
CTDloadder = function(genes, organism = "human", plotnumber = 30,  url = F){

  # Read data base
  numportertotalunir <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/CTD_humandatostotales.csv"), header = T)


  # We will count the number of genes per phenotype
  panelunir <- PhenoGeneNumber(genes, database = "CTD", organism)

  # We join the dataframes
  triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

  # Save diseases terms with at least 5 genes
  triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

  # To have two DF with different names
  triucusin <- triucu

  # Add gene overlap_ratio
  triucusin <- triucusin %>%
    dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


  # Calculate p.value (genes in each database)
  triucusin <- triucusin %>%
    dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19636 - genes_associated_in_db, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
    dplyr::mutate(pvalue = phyper(gene_overlap -1, genes_associated_in_db, 19636 - genes_associated_in_db, length(genes), lower.tail = FALSE))


  # Order by pvalue
  triucusin = triucusin[order(triucusin$adjust_pvalue),]
  triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

  # Get each gene per phenotype
  qglist <- QueryGenes(genes,database = "CTD", organism )

  if (url) {
  # Get the url for gene
  qglist$symbol <- paste0("<a href='https://www.genecards.org/cgi-bin/carddisp.pl?gene=", qglist$symbol,"'>", qglist$symbol,"</a>")
}
  # Collapse genes per phenotype
  listadegenesqg <- qglist %>%
    group_by(term_id) %>%
    mutate(common_genes = paste0(symbol, collapse = ", "))

  # Unique
  listadegenesqg <- unique(listadegenesqg[,c(3,6)])

  # Join genes and dataframe
  triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

  # Esto es para decidir el número de términos para plotear
  triucu = triucusin[1:plotnumber,]


  # Make interactive table with phenotype url
  htmlttriucu <- triucusin
  if (url) {
   htmlttriucu$term_id <- paste0("<a href='https://www.ncbi.nlm.nih.gov/medgen/", htmlttriucu$term_id,"'>", htmlttriucu$term_id,"</a>")
}
  newList <- list("alldata" = triucusin, "htmltabla" = htmlttriucu)
  return(newList)
}

#' @title ORPHANETloadder
#' @export
ORPHANETloadder = function(genes, organism = "human", plotnumber = 30,  url = F){

  # Read data base
  numportertotalunir <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/ORPHANETdatostotales.csv"), header = T)


  # We will count the number of genes per phenotype
  panelunir <- PhenoGeneNumber(genes, database = "ORPHANET", organism)

  # We join the dataframes
  triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

  # Save diseases terms with at least 5 genes
  triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

  # To have two DF with different names
  triucusin <- triucu

  # Add gene overlap_ratio
  triucusin <- triucusin %>%
    dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


  # Calculate p.value (genes in each database)
  triucusin <- triucusin %>%
    dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19262 - genes_associated_in_db, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
    dplyr::mutate(pvalue = phyper(gene_overlap -1, genes_associated_in_db, 19262 - genes_associated_in_db, length(genes), lower.tail = FALSE))


  # Order by pvalue
  triucusin = triucusin[order(triucusin$adjust_pvalue),]
  triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

  # Get each gene per phenotype
  qglist <- QueryGenes(genes,database = "ORPHANET", organism )

  # Get the url for gene
  if (url) {
  qglist$symbol <- paste0("<a href='https://www.genecards.org/cgi-bin/carddisp.pl?gene=", qglist$symbol,"'>", qglist$symbol,"</a>")
}
  # Collapse genes per phenotype
  listadegenesqg <- qglist %>%
    group_by(term_id) %>%
    mutate(common_genes = paste0(symbol, collapse = ", "))

  # Unique
  listadegenesqg <- unique(listadegenesqg[,c(3,6)])

  # Join genes and dataframe
  triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

  # Esto es para decidir el número de términos para plotear
  triucu = triucusin[1:plotnumber,]


  # Make interactive table with phenotype url
  htmlttriucu <- triucusin
  if (url) {
   htmlttriucu$term_id <- paste0("<a href='https://www.ncbi.nlm.nih.gov/medgen/", htmlttriucu$term_id,"'>", htmlttriucu$term_id,"</a>")
}
  newList <- list("alldata" = triucusin, "htmltabla" = htmlttriucu)
  return(newList)
}

#' @title PSYGENETloadder
#' @export
PSYGENETloadder = function(genes, organism = "human", plotnumber = 30,  url = F){

  # Read data base
  numportertotalunir <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/PSYGENETdatostotales.csv"), header = T)


  # We will count the number of genes per phenotype
  panelunir <- PhenoGeneNumber(genes, database = "PSYGENET", organism)

  # We join the dataframes
  triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

  # Save diseases terms with at least 5 genes
  triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

  # To have two DF with different names
  triucusin <- triucu

  # Add gene overlap_ratio
  triucusin <- triucusin %>%
    dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


  # Calculate p.value (genes in each database)
  triucusin <- triucusin %>%
    dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19262 - genes_associated_in_db, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
    dplyr::mutate(pvalue = phyper(gene_overlap -1, genes_associated_in_db, 19262 - genes_associated_in_db, length(genes), lower.tail = FALSE))


  # Order by pvalue
  triucusin = triucusin[order(triucusin$adjust_pvalue),]
  triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

  # Get each gene per phenotype
  qglist <- QueryGenes(genes,database = "PSYGENET", organism )

  # Get the url for gene
  if (url) {
  qglist$symbol <- paste0("<a href='https://www.genecards.org/cgi-bin/carddisp.pl?gene=", qglist$symbol,"'>", qglist$symbol,"</a>")
  }

  # Collapse genes per phenotype
  listadegenesqg <- qglist %>%
    group_by(term_id) %>%
    mutate(common_genes = paste0(symbol, collapse = ", "))

  # Unique
  listadegenesqg <- unique(listadegenesqg[,c(3,6)])

  # Join genes and dataframe
  triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

  # Esto es para decidir el número de términos para plotear
  triucu = triucusin[1:plotnumber,]


  # Make interactive table with phenotype url
  htmlttriucu <- triucusin
  if (url) {
  htmlttriucu$term_id <- paste0("<a href='https://www.ncbi.nlm.nih.gov/medgen/", htmlttriucu$term_id,"'>", htmlttriucu$term_id,"</a>")
}
  newList <- list("alldata" = triucusin, "htmltabla" = htmlttriucu)
  return(newList)
}

#' @title CGIloadder
#' @export
CGIloadder = function(genes, organism = "human", plotnumber = 30,  url = F){

  # Read data base
  numportertotalunir <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/CGIdatostotales.csv"), header = T)


  # We will count the number of genes per phenotype
  panelunir <- PhenoGeneNumber(genes, database = "CGI", organism)

  # We join the dataframes
  triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

  # Save diseases terms with at least 5 genes
  triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

  # To have two DF with different names
  triucusin <- triucu

  # Add gene overlap_ratio
  triucusin <- triucusin %>%
    dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


  # Calculate p.value (genes in each database)
  triucusin <- triucusin %>%
    dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19198 - genes_associated_in_db, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
    dplyr::mutate(pvalue = phyper(gene_overlap -1, genes_associated_in_db, 19198 - genes_associated_in_db, length(genes), lower.tail = FALSE))


  # Order by pvalue
  triucusin = triucusin[order(triucusin$adjust_pvalue),]
  triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

  # Get each gene per phenotype
  qglist <- QueryGenes(genes,database = "CGI", organism )

  # Get the url for gene
  if (url) {
  qglist$symbol <- paste0("<a href='https://www.genecards.org/cgi-bin/carddisp.pl?gene=", qglist$symbol,"'>", qglist$symbol,"</a>")
}
  # Collapse genes per phenotype
  listadegenesqg <- qglist %>%
    group_by(term_id) %>%
    mutate(common_genes = paste0(symbol, collapse = ", "))

  # Unique
  listadegenesqg <- unique(listadegenesqg[,c(3,6)])

  # Join genes and dataframe
  triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

  # Esto es para decidir el número de términos para plotear
  triucu = triucusin[1:plotnumber,]


  # Make interactive table with phenotype url
  htmlttriucu <- triucusin
  if (url) {
   htmlttriucu$term_id <- paste0("<a href='https://www.ncbi.nlm.nih.gov/medgen/", htmlttriucu$term_id,"'>", htmlttriucu$term_id,"</a>")
  }

  newList <- list("alldata" = triucusin, "htmltabla" = htmlttriucu)
  return(newList)
}

#' @title UNIPROTloadder
#' @export
UNIPROTloadder = function(genes, organism = "human", plotnumber = 30,  url = F){

  # Read data base
  numportertotalunir <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/UNIPROTdatostotales.csv"), header = T)


  # We will count the number of genes per phenotype
  panelunir <- PhenoGeneNumber(genes, database = "UNIPROT", organism)

  # We join the dataframes
  triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

  # Save diseases terms with at least 5 genes
  triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

  # To have two DF with different names
  triucusin <- triucu

  # Add gene overlap_ratio
  triucusin <- triucusin %>%
    dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


  # Calculate p.value (genes in each database)
  triucusin <- triucusin %>%
    dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19204 - genes_associated_in_db, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
    dplyr::mutate(pvalue = phyper(gene_overlap -1, genes_associated_in_db, 19204 - genes_associated_in_db, length(genes), lower.tail = FALSE))


  # Order by pvalue
  triucusin = triucusin[order(triucusin$adjust_pvalue),]
  triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

  # Get each gene per phenotype
  qglist <- QueryGenes(genes,database = "UNIPROT", organism )

  # Get the url for gene
  if (url) {
  qglist$symbol <- paste0("<a href='https://www.genecards.org/cgi-bin/carddisp.pl?gene=", qglist$symbol,"'>", qglist$symbol,"</a>")
}
  # Collapse genes per phenotype
  listadegenesqg <- qglist %>%
    group_by(term_id) %>%
    mutate(common_genes = paste0(symbol, collapse = ", "))

  # Unique
  listadegenesqg <- unique(listadegenesqg[,c(3,6)])

  # Join genes and dataframe
  triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

  # Esto es para decidir el número de términos para plotear
  triucu = triucusin[1:plotnumber,]


  # Make interactive table with phenotype url
  htmlttriucu <- triucusin
  if (url) {
   htmlttriucu$term_id <- paste0("<a href='https://www.ncbi.nlm.nih.gov/medgen/", htmlttriucu$term_id,"'>", htmlttriucu$term_id,"</a>")
}
  newList <- list("alldata" = triucusin, "htmltabla" = htmlttriucu)
  return(newList)
}


#' @title HPOloadderOpt
#' @export

HPOloadderOpt = function(genes, organism = "human", plotnumber = 30){

  # Read data base
  numportertotalunir <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/hpodatatotales.csv"), header = T)

  # We will count the number of genes per phenotype
  panelunir <- PhenoGeneNumberOpt(genes, database = "HPO", organism)

  # We join the dataframes
  triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

  # Save phenotypes with at least 10 genes
  triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)

  # To have two DF with different names
  triucusin <- triucu

  # Add gene overlap_ratio
  triucusin <- triucusin %>%
    dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


  # Calculate p.value (genes in each database)
  triucusin <- triucusin %>%
    dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19248 - genes_associated_in_db, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
    dplyr::mutate(pvalue = phyper(gene_overlap -1, genes_associated_in_db, 19248 - genes_associated_in_db, length(genes), lower.tail = FALSE))


  # Order by pvalue
  triucusin = triucusin[order(triucusin$adjust_pvalue),]
  triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

}

#' @title MGDloadderOpt
#' @export
MGDloadderOpt = function(genes, organism = "human", plotnumber = 30){
  # Read data base
  numportertotalunir <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/totalmponumber.tsv"), header = T, sep = "\t")


  # We will count the number of genes per phenotype
  panelunir <- PhenoGeneNumberOpt(genes, database = "MGD", organism)

  # We join the dataframes
  triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

  # Save phenotypes with at least 10 genes
  triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)

  # To have two DF with different names
  triucusin <- triucu

  # Add gene overlap_ratio
  triucusin <- triucusin %>%
    dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


  # Calculate p.value (genes in each database)
  triucusin <- triucusin %>%
    dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 17895 - genes_associated_in_db, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
    dplyr::mutate(pvalue = phyper(gene_overlap -1, genes_associated_in_db, 17895 - genes_associated_in_db, length(genes), lower.tail = FALSE))


  # Order by pvalue
  triucusin = triucusin[order(triucusin$adjust_pvalue),]
  triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]


}

#' @title CRBloadderOpt
#' @export

CRBloadderOpt = function(genes, organism = "human", plotnumber = 30){
  # Read data base
  numportertotalunir <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/cbrtotalnumber.csv"), header = T)


  # We will count the number of genes per phenotype
  panelunir <- PhenoGeneNumberOpt(genes, database = "CRB", organism)

  # We join the dataframes
  triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

  # Save phenotypes with at least 10 genes
  triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)

  # To have two DF with different names
  triucusin <- triucu

  # Add gene overlap_ratio
  triucusin <- triucusin %>%
    dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


  # Calculate p.value (genes in each database)
  triucusin <- triucusin %>%
    dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19274 - genes_associated_in_db, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
    dplyr::mutate(pvalue = phyper(gene_overlap -1, genes_associated_in_db, 19274 - genes_associated_in_db, length(genes), lower.tail = FALSE))


  # Order by pvalue
  triucusin = triucusin[order(triucusin$adjust_pvalue),]
  triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]
}

#' @title CLINGENloadderOpt
#' @export


CLINGENloadderOpt = function(genes, organism = "human", plotnumber = 30){

  # Read data base
  numportertotalunir <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/clingendatostotales.csv"), header = T)


  # We will count the number of genes per phenotype
  panelunir <- PhenoGeneNumberOpt(genes, database = "CLINGEN", organism)

  # We join the dataframes
  triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

  # Save diseases terms with at least 5 genes
  triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

  # To have two DF with different names
  triucusin <- triucu

  # Add gene overlap_ratio
  triucusin <- triucusin %>%
    dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


  # Calculate p.value (genes in each database)
  triucusin <- triucusin %>%
    dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19198 - genes_associated_in_db, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
    dplyr::mutate(pvalue = phyper(gene_overlap -1, genes_associated_in_db, 19198 - genes_associated_in_db, length(genes), lower.tail = FALSE))


  # Order by pvalue
  triucusin = triucusin[order(triucusin$adjust_pvalue),]
  triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]


}

#' @title GENOMICS_ENGLANDloadderOpt
#' @export


GENOMICS_ENGLANDloadderOpt = function(genes, organism = "human", plotnumber = 30){

  # Read data base
  numportertotalunir <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                     "/GENOMICS_ENGLANDdatostotales.csv"), header = T)

  # We will count the number of genes per phenotype
  panelunir <- PhenoGeneNumberOpt(genes, database = "GENOMICS_ENGLAND", organism)

  # We join the dataframes
  triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

  # Save diseases terms with at least 5 genes
  triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

  # To have two DF with different names
  triucusin <- triucu

  # Add gene overlap_ratio
  triucusin <- triucusin %>%
    dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


  # Calculate p.value (genes in each database)
  triucusin <- triucusin %>%
    dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19230 - genes_associated_in_db, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
    dplyr::mutate(pvalue = phyper(gene_overlap -1, genes_associated_in_db, 19230 - genes_associated_in_db, length(genes), lower.tail = FALSE))


  # Order by pvalue
  triucusin = triucusin[order(triucusin$adjust_pvalue),]
  triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]


}


#' @title CTDloadderOpt
#' @export

CTDloadderOpt = function(genes, organism = "human", plotnumber = 30){

# Read data base
numportertotalunir <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                   "/CTD_humandatostotales.csv"), header = T)

# We will count the number of genes per phenotype
panelunir <- PhenoGeneNumberOpt(genes, database = "CTD", organism)

# We join the dataframes
triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

# Save diseases terms with at least 5 genes
triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

# To have two DF with different names
triucusin <- triucu

# Add gene overlap_ratio
triucusin <- triucusin %>%
  dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


# Calculate p.value (genes in each database)
triucusin <- triucusin %>%
  dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19636 - genes_associated_in_db, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
  dplyr::mutate(pvalue = phyper(gene_overlap -1, genes_associated_in_db, 19636 - genes_associated_in_db, length(genes), lower.tail = FALSE))


# Order by pvalue
triucusin = triucusin[order(triucusin$adjust_pvalue),]
triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]


}


#' @title ORPHANETloadderOpt
#' @export


 ORPHANETloadderOpt = function(genes, organism = "human", plotnumber = 30){

# Read data base
numportertotalunir <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                   "/ORPHANETdatostotales.csv"), header = T)

# We will count the number of genes per phenotype
panelunir <- PhenoGeneNumberOpt(genes, database = "ORPHANET", organism)

# We join the dataframes
triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

# Save diseases terms with at least 5 genes
triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

# To have two DF with different names
triucusin <- triucu

# Add gene overlap_ratio
triucusin <- triucusin %>%
  dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


# Calculate p.value (genes in each database)
triucusin <- triucusin %>%
  dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19262 - genes_associated_in_db, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
  dplyr::mutate(pvalue = phyper(gene_overlap -1, genes_associated_in_db, 19262 - genes_associated_in_db, length(genes), lower.tail = FALSE))


# Order by pvalue
triucusin = triucusin[order(triucusin$adjust_pvalue),]
triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

}


#' @title PSYGENETloadderOpt
#' @export

 PSYGENETloadderOpt = function(genes, organism = "human", plotnumber = 30){

   # Read data base
   numportertotalunir <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                      "/PSYGENETdatostotales.csv"), header = T)

   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumberOpt(genes, database = "PSYGENET", organism)

   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save diseases terms with at least 5 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene overlap_ratio
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value (genes in each database)
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19262 - genes_associated_in_db, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
     dplyr::mutate(pvalue = phyper(gene_overlap -1, genes_associated_in_db, 19262 - genes_associated_in_db, length(genes), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

 }


#' @title UNIPROTloadderOpt
#' @export
 UNIPROTloadderOpt = function(genes, organism = "human", plotnumber = 30){

   # Read data base
   numportertotalunir <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                      "/UNIPROTdatostotales.csv"), header = T)

   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumberOpt(genes, database = "UNIPROT", organism)

   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save diseases terms with at least 5 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene overlap_ratio
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value (genes in each database)
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19204 - genes_associated_in_db, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
     dplyr::mutate(pvalue = phyper(gene_overlap -1, genes_associated_in_db, 19204 - genes_associated_in_db, length(genes), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

 }



#' @title CGIloadderOpt
#' @export

 CGIloadderOpt = function(genes, organism = "human", plotnumber = 30){

   # Read data base
   numportertotalunir <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                                      "/CGIdatostotales.csv"), header = T)

   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumberOpt(genes, database = "CGI", organism)

   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save diseases terms with at least 5 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene overlap_ratio
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value (genes in each database)
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, 19198 - genes_associated_in_db, length(genes), lower.tail = FALSE), method = "bonferroni")) %>%
     dplyr::mutate(pvalue = phyper(gene_overlap -1, genes_associated_in_db, 19198 - genes_associated_in_db, length(genes), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

 }

 #' @title HPOloaddercompare
 #' @export
 HPOloaddercompare = function(onlygenes, genesetcompare, organism = "human", plotnumber = 30,  url = F){

   # Read data base
   custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                          "/hpobasededatos.csv"), header = T)


   custom <- dplyr::anti_join(custom, onlygenes, by = "entrez")

   # We will count the number of genes per phenotype
   numportertotal <-   custom %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
   numportertotalunir <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
   names(numportertotalunir) <- c("term_id", "term_name", "genes_associated_in_db")
   numportertotalunir$source <- "HPO"


   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumber(genesetcompare, database = "HPO", organism)

   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save phenotypes with at least 10 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene overlap_ratio
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value (genes in each database)
   # Calculate p.value
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE), method = "bonferroni"))%>%
     dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

   # Get each gene per phenotype
   qglist <- QueryGenes(genesetcompare, database = "HPO", organism )

   if (url) {

   # Get the url for gene
   qglist$symbol <- paste0("<a href='https://www.genecards.org/cgi-bin/carddisp.pl?gene=", qglist$symbol,"'>", qglist$symbol,"</a>")

   }

   # Collapse genes per phenotype
   listadegenesqg <- qglist %>%
     group_by(term_id) %>%
     mutate(common_genes = paste0(symbol, collapse = ", "))

   # Unique names
   listadegenesqg <- unique(listadegenesqg[,c(3,6)])

   # Join genes and dataframe
   triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

   # Esto es para decidir el número de términos para plotear
   triucu = triucusin[1:plotnumber,]


   # Make interactive table with phenotype url
   htmlttriucu <- triucusin
   if (url) {
   htmlttriucu$term_id <- paste0("<a href='https://hpo.jax.org/app/browse/term/", htmlttriucu$term_id,"'>", htmlttriucu$term_id,"</a>")
}

   # Return list
   newList <- list("alldata" = triucusin, "htmltabla" = htmlttriucu)
   return(newList)
 }


 #' @title MGDloaddercompare
 #' @export


 MGDloaddercompare = function(onlygenes,genesetcompare, organism = "human", plotnumber = 30,  url = F){
 custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                        "/mgibasefinal.tsv"), header = T, sep = "\t")

 custom <- dplyr::anti_join(custom, onlygenes, by = "entrez")

 # We will count the number of genes per phenotype
 numportertotal <-   custom %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
 numportertotalunir <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
 names(numportertotalunir) <- c("term_id", "term_name", "genes_associated_in_db")
 numportertotalunir$source <- "MGD"


 # We will count the number of genes per phenotype
 panelunir <- PhenoGeneNumber(genesetcompare, database = "MGD",organism)

 # We join the dataframes
 triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

 # Save phenotypes with at least 10 genes
 triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)

 # To have two DF with different names
 triucusin <- triucu

 # Add gene total_size_ratio col
 triucusin <- triucusin %>%
   dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


 # Calculate p.value
 triucusin <- triucusin %>%
   dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$entrez)), length(genesetcompare), lower.tail = FALSE), method = "bonferroni"))%>%
   dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$entrez)), length(genesetcompare), lower.tail = FALSE))


 # Order by pvalue
 triucusin = triucusin[order(triucusin$adjust_pvalue),]
 triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

 # Get each gene per phenotype
 qglist <- QueryGenes(genesetcompare,database = "MGD", organism )

 if (url) {
 # Get the url for gene
 qglist$symbol <- paste0("<a href='https://www.genecards.org/cgi-bin/carddisp.pl?gene=", qglist$symbol,"'>", qglist$symbol,"</a>")
}
 # Collapse genes per phenotype
 listadegenesqg <- qglist %>%
   group_by(term_id) %>%
   mutate(common_genes = paste0(symbol, collapse = ", "))

 # Unique
 listadegenesqg <- unique(listadegenesqg[,c(3,5)])

 # Join genes and dataframe
 triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

 # Esto es para decidir el número de términos para plotear
 triucu = triucusin[1:plotnumber,]


 # Make interactive table with phenotype url
 htmlttriucu <- triucusin

 if (url) {
 htmlttriucu$term_id <- paste0("<a href='http://www.informatics.jax.org/vocab/mp_ontology/", htmlttriucu$term_id,"'>", htmlttriucu$term_id,"</a>")
 }

 newList <- list("alldata" = triucusin, "htmltabla" = htmlttriucu)
 return(newList)
 }


 #' @title CRBloaddercompare
 #' @export

 CRBloaddercompare = function(onlygenes, genesetcompare, organism = "human", plotnumber = 30,  url = F){
   # Read data base
   custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                          "/cbrdatabaseok.csv"), header = T)

   custom <- dplyr::anti_join(custom, onlygenes, by = "entrez")

   # We will count the number of genes per phenotype
   numportertotal <-   custom %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
   numportertotalunir <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
   names(numportertotalunir) <- c("term_id", "term_name", "genes_associated_in_db")
   numportertotalunir$source <- "CRB"


   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumber(genesetcompare,database = "CRB",organism)
   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save phenotypes with at least 10 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene overlap_ratio
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value (genes in each database)
   # Calculate p.value
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE), method = "bonferroni"))%>%
     dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

   # Get each gene per phenotype
   qglist <- QueryGenes(genesetcompare,database = "CRB", organism )

   # Get the url for gene
   if (url) {
     qglist$symbol <- paste0("<a href='https://www.genecards.org/cgi-bin/carddisp.pl?gene=", qglist$symbol,"'>", qglist$symbol,"</a>")
}
   # Collapse genes per phenotype
   listadegenesqg <- qglist %>%
     group_by(term_id) %>%
     mutate(common_genes = paste0(symbol, collapse = ", "))

   # Unique
   listadegenesqg <- unique(listadegenesqg[,c(4,6)])

   # Join genes and dataframe
   triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

   # Esto es para decidir el número de términos para plotear
   triucu = triucusin[1:plotnumber,]


   # Make interactive table with phenotype url
   htmlttriucu <- triucusin
   if (url) {
     htmlttriucu$term_id <- paste0("<a href='https://crisprbrain.org/simple-screen/", htmlttriucu$term_id,"'>", htmlttriucu$term_id,"</a>")
}

   newList <- list("alldata" = triucusin, "htmltabla" = htmlttriucu)
   return(newList)
 }


 #' @title CLINGENloaddercompare
 #' @export

 CLINGENloaddercompare = function(onlygenes,genesetcompare, organism = "human", plotnumber = 30,  url = F){
   custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                          "/clingenbasededatos.csv"), header = T)

   custom <- dplyr::anti_join(custom, onlygenes, by = "entrez")

   # We will count the number of genes per phenotype
   numportertotal <-   custom %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
   numportertotalunir <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
   names(numportertotalunir) <- c("term_id", "term_name", "genes_associated_in_db")
   numportertotalunir$source <- "CLINGEN"

   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumber(genesetcompare, database = "CLINGEN", organism)

   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save diseases terms with at least 5 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene overlap_ratio
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value (genes in each database)
   # Calculate p.value
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE), method = "bonferroni"))%>%
     dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

   # Get each gene per phenotype
   qglist <- QueryGenes(genesetcompare,database = "CLINGEN", organism )

   if (url) {
   # Get the url for gene
   qglist$symbol <- paste0("<a href='https://www.genecards.org/cgi-bin/carddisp.pl?gene=", qglist$symbol,"'>", qglist$symbol,"</a>")
}
   # Collapse genes per phenotype
   listadegenesqg <- qglist %>%
     group_by(term_id) %>%
     mutate(common_genes = paste0(symbol, collapse = ", "))

   # Unique
   listadegenesqg <- unique(listadegenesqg[,c(3,6)])

   # Join genes and dataframe
   triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

   # Esto es para decidir el número de términos para plotear
   triucu = triucusin[1:plotnumber,]


   # Make interactive table with phenotype url
   htmlttriucu <- triucusin
   if (url) {
     htmlttriucu$term_id <- paste0("<a href='https://www.ncbi.nlm.nih.gov/medgen/", htmlttriucu$term_id,"'>", htmlttriucu$term_id,"</a>")
}
   newList <- list("alldata" = triucusin, "htmltabla" = htmlttriucu)
   return(newList)
 }


 #' @title GENOMICS_ENGLANDloaddercompare
 #' @export

 GENOMICS_ENGLANDloaddercompare = function(onlygenes,genesetcompare, organism = "human", plotnumber = 30,  url = F){

   # Read data base
   custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                          "/GENOMICS_ENGLANDbasededatos.csv"), header = T)

   custom <- dplyr::anti_join(custom, onlygenes, by = "entrez")

   # We will count the number of genes per phenotype
   numportertotal <-   custom %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
   numportertotalunir <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
   names(numportertotalunir) <- c("term_id", "term_name", "genes_associated_in_db")
   numportertotalunir$source <- "GENOMIC_ENGLAND"


   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumber(genesetcompare, database = "GENOMICS_ENGLAND", organism)

   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save diseases terms with at least 5 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene overlap_ratio
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value (genes in each database)
   # Calculate p.value
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE), method = "bonferroni"))%>%
     dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

   # Get each gene per phenotype
   qglist <- QueryGenes(genesetcompare,database = "GENOMICS_ENGLAND", organism )
   if (url) {
   # Get the url for gene
   qglist$symbol <- paste0("<a href='https://www.genecards.org/cgi-bin/carddisp.pl?gene=", qglist$symbol,"'>", qglist$symbol,"</a>")
}
   # Collapse genes per phenotype
   listadegenesqg <- qglist %>%
     group_by(term_id) %>%
     mutate(common_genes = paste0(symbol, collapse = ", "))

   # Unique
   listadegenesqg <- unique(listadegenesqg[,c(3,6)])

   # Join genes and dataframe
   triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

   # Esto es para decidir el número de términos para plotear
   triucu = triucusin[1:plotnumber,]


   # Make interactive table with phenotype url
   htmlttriucu <- triucusin
   if (url) {
     htmlttriucu$term_id <- paste0("<a href='https://www.ncbi.nlm.nih.gov/medgen/", htmlttriucu$term_id,"'>", htmlttriucu$term_id,"</a>")
}
   newList <- list("alldata" = triucusin, "htmltabla" = htmlttriucu)
   return(newList)
 }


 #' @title CTDloaddercompare
 #' @export

 CTDloaddercompare = function(onlygenes,genesetcompare, organism = "human", plotnumber = 30,  url = F){
   custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                          "/CTD_humanbasededatos.csv"), header = T)


   custom <- dplyr::anti_join(custom, onlygenes, by = "entrez")

   # We will count the number of genes per phenotype
   numportertotal <-   custom %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
   numportertotalunir <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
   names(numportertotalunir) <- c("term_id", "term_name", "genes_associated_in_db")
   numportertotalunir$source <- "CTD"


   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumber(genesetcompare, database = "CTD", organism)

   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save diseases terms with at least 5 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene overlap_ratio
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value (genes in each database)
   # Calculate p.value
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE), method = "bonferroni"))%>%
     dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

   # Get each gene per phenotype
   qglist <- QueryGenes(genesetcompare,database = "CTD", organism )

   if (url) {
   # Get the url for gene
   qglist$symbol <- paste0("<a href='https://www.genecards.org/cgi-bin/carddisp.pl?gene=", qglist$symbol,"'>", qglist$symbol,"</a>")
   }

   # Collapse genes per phenotype
   listadegenesqg <- qglist %>%
     group_by(term_id) %>%
     mutate(common_genes = paste0(symbol, collapse = ", "))

   # Unique
   listadegenesqg <- unique(listadegenesqg[,c(3,6)])

   # Join genes and dataframe
   triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

   # Esto es para decidir el número de términos para plotear
   triucu = triucusin[1:plotnumber,]


   # Make interactive table with phenotype url
   htmlttriucu <- triucusin
   if (url) {
   htmlttriucu$term_id <- paste0("<a href='https://www.ncbi.nlm.nih.gov/medgen/", htmlttriucu$term_id,"'>", htmlttriucu$term_id,"</a>")
   }

   newList <- list("alldata" = triucusin, "htmltabla" = htmlttriucu)
   return(newList)
 }


 #' @title ORPHANETloaddercompare
 #' @export

 ORPHANETloaddercompare = function(onlygenes,genesetcompare, organism = "human", plotnumber = 30,  url = F){
   custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                          "/ORPHANETbasededatos.csv"), header = T)


   custom <- dplyr::anti_join(custom, onlygenes, by = "entrez")

   # We will count the number of genes per phenotype
   numportertotal <-   custom %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
   numportertotalunir <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
   names(numportertotalunir) <- c("term_id", "term_name", "genes_associated_in_db")

   numportertotalunir$source <- "ORPHANET"


   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumber(genesetcompare, database = "ORPHANET", organism)

   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save diseases terms with at least 5 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene overlap_ratio
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value (genes in each database)
   # Calculate p.value
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE), method = "bonferroni"))%>%
     dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

   # Get each gene per phenotype
   qglist <- QueryGenes(genesetcompare,database = "ORPHANET", organism )

   if (url) {
   # Get the url for gene
   qglist$symbol <- paste0("<a href='https://www.genecards.org/cgi-bin/carddisp.pl?gene=", qglist$symbol,"'>", qglist$symbol,"</a>")
}
   # Collapse genes per phenotype
   listadegenesqg <- qglist %>%
     group_by(term_id) %>%
     mutate(common_genes = paste0(symbol, collapse = ", "))

   # Unique
   listadegenesqg <- unique(listadegenesqg[,c(3,6)])

   # Join genes and dataframe
   triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

   # Esto es para decidir el número de términos para plotear
   triucu = triucusin[1:plotnumber,]


   # Make interactive table with phenotype url
   htmlttriucu <- triucusin
   if (url) {
     htmlttriucu$term_id <- paste0("<a href='https://www.ncbi.nlm.nih.gov/medgen/", htmlttriucu$term_id,"'>", htmlttriucu$term_id,"</a>")
}
   newList <- list("alldata" = triucusin, "htmltabla" = htmlttriucu)
   return(newList)
 }


 #' @title PSYGENETloaddercompare
 #' @export

 PSYGENETloaddercompare = function(onlygenes,genesetcompare, organism = "human", plotnumber = 30,  url = F){
   custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                          "/PSYGENETbasededatos.csv"), header = T)

   custom <- dplyr::anti_join(custom, onlygenes, by = "entrez")

   # We will count the number of genes per phenotype
   numportertotal <-   custom %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
   numportertotalunir <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
   names(numportertotalunir) <- c("term_id", "term_name", "genes_associated_in_db")
   numportertotalunir$source <- "PSYGENET"



   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumber(genesetcompare, database = "PSYGENET", organism)

   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save diseases terms with at least 5 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene overlap_ratio
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value (genes in each database)
   # Calculate p.value
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE), method = "bonferroni"))%>%
     dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

   # Get each gene per phenotype
   qglist <- QueryGenes(genesetcompare,database = "PSYGENET", organism )
   if (url) {
   # Get the url for gene
   qglist$symbol <- paste0("<a href='https://www.genecards.org/cgi-bin/carddisp.pl?gene=", qglist$symbol,"'>", qglist$symbol,"</a>")
}
   # Collapse genes per phenotype
   listadegenesqg <- qglist %>%
     group_by(term_id) %>%
     mutate(common_genes = paste0(symbol, collapse = ", "))

   # Unique
   listadegenesqg <- unique(listadegenesqg[,c(3,6)])

   # Join genes and dataframe
   triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

   # Esto es para decidir el número de términos para plotear
   triucu = triucusin[1:plotnumber,]


   # Make interactive table with phenotype url
   htmlttriucu <- triucusin
   if (url) {
     htmlttriucu$term_id <- paste0("<a href='https://www.ncbi.nlm.nih.gov/medgen/", htmlttriucu$term_id,"'>", htmlttriucu$term_id,"</a>")
}
   newList <- list("alldata" = triucusin, "htmltabla" = htmlttriucu)
   return(newList)
 }


 #' @title UNIPROTloaddercompare
 #' @export



 UNIPROTloaddercompare = function(onlygenes,genesetcompare, organism = "human", plotnumber = 30,  url = F){
   custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                          "/UNIPROTbasededatos.csv"), header = T)


   custom <- dplyr::anti_join(custom, onlygenes, by = "entrez")

   # We will count the number of genes per phenotype
   numportertotal <-   custom %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
   numportertotalunir <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
   names(numportertotalunir) <- c("term_id", "term_name", "genes_associated_in_db")

   numportertotalunir$source <- "UNIPROT"


   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumber(genesetcompare, database = "UNIPROT", organism)

   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save diseases terms with at least 5 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene overlap_ratio
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value (genes in each database)
   # Calculate p.value
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE), method = "bonferroni"))%>%
     dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]


   # Get each gene per phenotype
   qglist <- QueryGenes(genesetcompare,database = "UNIPROT", organism )

 if (url) {
   # Get the url for gene
   qglist$symbol <- paste0("<a href='https://www.genecards.org/cgi-bin/carddisp.pl?gene=", qglist$symbol,"'>", qglist$symbol,"</a>")
 }
   # Collapse genes per phenotype
   listadegenesqg <- qglist %>%
     group_by(term_id) %>%
     mutate(common_genes = paste0(symbol, collapse = ", "))

   # Unique
   listadegenesqg <- unique(listadegenesqg[,c(3,6)])

   # Join genes and dataframe
   triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

   # Esto es para decidir el número de términos para plotear
   triucu = triucusin[1:plotnumber,]


   # Make interactive table with phenotype url
   htmlttriucu <- triucusin
   if (url) {
     htmlttriucu$term_id <- paste0("<a href='https://www.ncbi.nlm.nih.gov/medgen/", htmlttriucu$term_id,"'>", htmlttriucu$term_id,"</a>")
}
   newList <- list("alldata" = triucusin, "htmltabla" = htmlttriucu)
   return(newList)
 }



 #' @title CGIloaddercompare
 #' @export

 CGIloaddercompare = function(onlygenes,genesetcompare, organism = "human", plotnumber = 30,  url = F){
   custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                          "/CGIbasededatos.csv"), header = T)


   custom <- dplyr::anti_join(custom, onlygenes, by = "entrez")

   # We will count the number of genes per phenotype
   numportertotal <-   custom %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
   numportertotalunir <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
   names(numportertotalunir) <- c("term_id", "term_name", "genes_associated_in_db")

   numportertotalunir$source <- "CGI"

   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumber(genesetcompare, database = "CGI", organism)

   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save diseases terms with at least 5 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene overlap_ratio
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value (genes in each database)
   # Calculate p.value
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE), method = "bonferroni"))%>%
     dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]


   # Get each gene per phenotype
   qglist <- QueryGenes(genesetcompare,database = "CGI", organism )
   if (url) {
   # Get the url for gene
   qglist$symbol <- paste0("<a href='https://www.genecards.org/cgi-bin/carddisp.pl?gene=", qglist$symbol,"'>", qglist$symbol,"</a>")
}
   # Collapse genes per phenotype
   listadegenesqg <- qglist %>%
     group_by(term_id) %>%
     mutate(common_genes = paste0(symbol, collapse = ", "))

   # Unique
   listadegenesqg <- unique(listadegenesqg[,c(3,6)])

   # Join genes and dataframe
   triucusin <- inner_join(triucusin,listadegenesqg, by = "term_id")

   # Esto es para decidir el número de términos para plotear
   triucu = triucusin[1:plotnumber,]


   # Make interactive table with phenotype url
   htmlttriucu <- triucusin
   if (url) {
     htmlttriucu$term_id <- paste0("<a href='https://www.ncbi.nlm.nih.gov/medgen/", htmlttriucu$term_id,"'>", htmlttriucu$term_id,"</a>")
}
   newList <- list("alldata" = triucusin, "htmltabla" = htmlttriucu)
   return(newList)
 }

 #' @title HPOloaddercompareOpt
 #' @export

 HPOloaddercompareOpt = function(onlygenes, geneset, genesetcompare, organism = "human", plotnumber = 30){

   # Read data base
   custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                          "/hpobasededatos.csv"), header = T)


   custom <- dplyr::anti_join(custom, onlygenes, by = "entrez")

   # We will count the number of genes per phenotype
   numportertotal <-   custom %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
   numportertotalunir <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
   names(numportertotalunir) <- c("term_id", "term_name", "genes_associated_in_db")
   numportertotalunir$source <- "HPO"

   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumberOpt(genesetcompare, database = "HPO", organism)

   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save phenotypes with at least 10 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene overlap_ratio
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value (genes in each database)
   # Calculate p.value
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE), method = "bonferroni"))%>%
     dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

   return(triucusin)
 }


 #' @title MGDloaddercompareOpt
 #' @export
 MGDloaddercompareOpt = function(onlygenes, geneset, genesetcompare, organism = "human", plotnumber = 30){
   custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                          "/mgibasefinal.tsv"), header = T, sep = "\t")

   names(custom) <- c("entrez","human_symbol", "mouse_symbol", "mgi", "term_id", "term_name", "info", "source"   )

   custom <- dplyr::anti_join(custom, onlygenes, by = "entrez")

   # We will count the number of genes per phenotype
   numportertotal <-   custom %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
   numportertotalunir <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
   names(numportertotalunir) <- c("term_id", "term_name", "genes_associated_in_db")
   numportertotalunir$source <- "MGD"


   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumberOpt(genesetcompare, database = "MGD",organism)

   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save phenotypes with at least 10 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene total_size_ratio col
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$entrez)), length(genesetcompare), lower.tail = FALSE), method = "bonferroni"))%>%
     dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$entrez)), length(genesetcompare), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

   return(triucusin)
 }



 #' @title CRBloaddercompareOpt
 #' @export

 CRBloaddercompareOpt = function(onlygenes, geneset, genesetcompare, organism = "human", plotnumber = 30){
   # Read data base
   custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                          "/cbrdatabaseok.csv"), header = T)



   custom <- dplyr::anti_join(custom, onlygenes, by = "entrez")

   # We will count the number of genes per phenotype
   numportertotal <-   custom %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
   numportertotalunir <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
   names(numportertotalunir) <- c("term_id", "term_name", "genes_associated_in_db")
   numportertotalunir$source <- "CRB"


   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumberOpt(genesetcompare,database = "CRB",organism)
   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save phenotypes with at least 10 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 10)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene overlap_ratio
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value (genes in each database)
   # Calculate p.value
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE), method = "bonferroni"))%>%
     dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]


   return(triucusin)
 }



 #' @title CLINGENloaddercompareOpt
 #' @export

 CLINGENloaddercompareOpt = function(onlygenes, geneset, genesetcompare, organism = "human", plotnumber = 30){
   custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                          "/clingenbasededatos.csv"), header = T)



   custom <- dplyr::anti_join(custom, onlygenes, by = "entrez")

   # We will count the number of genes per phenotype
   numportertotal <-   custom %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
   numportertotalunir <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
   names(numportertotalunir) <- c("term_id", "term_name", "genes_associated_in_db")
   numportertotalunir$source <- "CLINGEN"

   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumberOpt(genesetcompare, database = "CLINGEN", organism)

   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save diseases terms with at least 5 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene overlap_ratio
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value (genes in each database)
   # Calculate p.value
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE), method = "bonferroni"))%>%
     dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]


   return(triucusin)
 }


 #' @title GENOMICS_ENGLANDloaddercompareOpt
 #' @export

 GENOMICS_ENGLANDloaddercompareOpt = function(onlygenes, geneset, genesetcompare, organism = "human", plotnumber = 30){

   # Read data base
   custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                          "/GENOMICS_ENGLANDbasededatos.csv"), header = T)


   custom <- dplyr::anti_join(custom, onlygenes, by = "entrez")

   # We will count the number of genes per phenotype
   numportertotal <-   custom %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
   numportertotalunir <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
   names(numportertotalunir) <- c("term_id", "term_name", "genes_associated_in_db")
   numportertotalunir$source <- "GENOMICS_ENGLAND"


   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumberOpt(genesetcompare, database = "GENOMICS_ENGLAND", organism)

   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save diseases terms with at least 5 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene overlap_ratio
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value (genes in each database)
   # Calculate p.value
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE), method = "bonferroni"))%>%
     dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

   return(triucusin)
 }


 #' @title CTDloaddercompareOpt
 #' @export

 CTDloaddercompareOpt = function(onlygenes, geneset, genesetcompare, organism = "human", plotnumber = 30){
   custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                          "/CTD_humanbasededatos.csv"), header = T)



   custom <- dplyr::anti_join(custom, onlygenes, by = "entrez")

   # We will count the number of genes per phenotype
   numportertotal <-   custom %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
   numportertotalunir <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
   names(numportertotalunir) <- c("term_id", "term_name", "genes_associated_in_db")
   numportertotalunir$source <- "CTD"

   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumberOpt(genesetcompare, database = "CTD", organism)

   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save diseases terms with at least 5 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene overlap_ratio
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value (genes in each database)
   # Calculate p.value
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE), method = "bonferroni"))%>%
     dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]

   return(triucusin)
 }


 #' @title ORPHANETloaddercompareOpt
 #' @export

 ORPHANETloaddercompareOpt = function(onlygenes, geneset, genesetcompare, organism = "human", plotnumber = 30){
   custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                          "/ORPHANETbasededatos.csv"), header = T)



   custom <- dplyr::anti_join(custom, onlygenes, by = "entrez")

   # We will count the number of genes per phenotype
   numportertotal <-   custom %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
   numportertotalunir <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
   names(numportertotalunir) <- c("term_id", "term_name", "genes_associated_in_db")
   numportertotalunir$source <- "ORPHANET"

   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumberOpt(genesetcompare, database = "ORPHANET", organism)

   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save diseases terms with at least 5 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene overlap_ratio
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value (genes in each database)
   # Calculate p.value
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE), method = "bonferroni"))%>%
     dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]


   return(triucusin)
 }


 #' @title PSYGENETloaddercompareOpt
 #' @export

 PSYGENETloaddercompareOpt = function(onlygenes, geneset, genesetcompare, organism = "human", plotnumber = 30){
   custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                          "/PSYGENETbasededatos.csv"), header = T)



   custom <- dplyr::anti_join(custom, onlygenes, by = "entrez")

   # We will count the number of genes per phenotype
   numportertotal <-   custom %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
   numportertotalunir <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
   names(numportertotalunir) <- c("term_id", "term_name", "genes_associated_in_db")
   numportertotalunir$source <- "PSYGENET"

   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumberOpt(genesetcompare, database = "PSYGENET", organism)

   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save diseases terms with at least 5 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene overlap_ratio
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value (genes in each database)
   # Calculate p.value
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE), method = "bonferroni"))%>%
     dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]


   return(triucusin)
 }


 #' @title UNIPROTloaddercompareOpt
 #' @export
 UNIPROTloaddercompareOpt = function(onlygenes, geneset, genesetcompare, organism = "human", plotnumber = 30){
   custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                          "/UNIPROTbasededatos.csv"), header = T)



   custom <- dplyr::anti_join(custom, onlygenes, by = "entrez")

   # We will count the number of genes per phenotype
   numportertotal <-   custom %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
   numportertotalunir <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
   names(numportertotalunir) <- c("term_id", "term_name", "genes_associated_in_db")
   numportertotalunir$source <- "UNIPROT"

   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumberOpt(genesetcompare, database = "UNIPROT", organism)

   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save diseases terms with at least 5 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene overlap_ratio
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value (genes in each database)
   # Calculate p.value
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE), method = "bonferroni"))%>%
     dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]


   return(triucusin)
 }



 #' @title CGIloaddercompareOpt
 #' @export
 CGIloaddercompareOpt = function(onlygenes, geneset, genesetcompare, organism = "human", plotnumber = 30){
   custom <- fread(paste0(path.package("PhenoExamWeb", quiet = FALSE),
                          "/CGIbasededatos.csv"), header = T)



   custom <- dplyr::anti_join(custom, onlygenes, by = "entrez")

   # We will count the number of genes per phenotype
   numportertotal <-   custom %>% dplyr::group_by(term_id, term_name) %>% dplyr::tally()
   numportertotalunir <- dplyr::arrange(numportertotal, dplyr::desc(numportertotal$n))
   names(numportertotalunir) <- c("term_id", "term_name", "genes_associated_in_db")
   numportertotalunir$source <- "CGI"

   # We will count the number of genes per phenotype
   panelunir <- PhenoGeneNumberOpt(genesetcompare, database = "CGI", organism)

   # We join the dataframes
   triucu <- merge.data.frame(numportertotalunir,panelunir, by = c("term_id","term_name"))

   # Save diseases terms with at least 5 genes
   triucu <- dplyr::filter(triucu, triucu$genes_associated_in_db >= 5)

   # To have two DF with different names
   triucusin <- triucu

   # Add gene overlap_ratio
   triucusin <- triucusin %>%
     dplyr::mutate(overlap_ratio = paste0("(",gene_overlap,"/",genes_associated_in_db,")"))


   # Calculate p.value (genes in each database)
   # Calculate p.value
   triucusin <- triucusin %>%
     dplyr::mutate(adjust_pvalue = p.adjust(phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE), method = "bonferroni"))%>%
     dplyr::mutate(pvalue = phyper(gene_overlap - 1, genes_associated_in_db, length(unique(custom$symbol)) - genes_associated_in_db, length(genesetcompare), lower.tail = FALSE))


   # Order by pvalue
   triucusin = triucusin[order(triucusin$adjust_pvalue),]
   triucusin = triucusin[,c(1:2,4,7,3,5,6,8)]


   return(triucusin)
 }
alexcis95/PhenoExamWeb documentation built on April 24, 2021, 3:07 p.m.
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alexcis95/PhenoExamWeb
Compare the likeness of phenotypes terms between groups of genes

R/querygenescrb.R
In alexcis95/PhenoExamWeb: Compare the likeness of phenotypes terms between groups of genes

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alexcis95/PhenoExamWeb Compare the likeness of phenotypes terms between groups of genes

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alexcis95/PhenoExamWeb
Compare the likeness of phenotypes terms between groups of genes

R/querygenescrb.R
In alexcis95/PhenoExamWeb: Compare the likeness of phenotypes terms between groups of genes
