R/enrich_kegg2.R

In jaspershen/metPath: Pathway analysis for metabolomics data

#' @title enrich_kegg
#' @description Pathway enrichment for SMPDB database.
#' @author Xiaotao Shen
#' \email{shenxt@@sioc.ac.cn}
#' @param query_id The vector of query IDs.
#' @param query_type "compound" or "gene"
#' @param id_type HMDB
#' @param pathway_database KEGG or other metabolomics pathway database.
#' @param p_cutoff p_cutoff
#' @param p_adjust_method p_adjust_method
#' @param method Hypergeometric or fisher test.
#' @param threads threads
#' @return  The MSE analysis result.
#' @export

# load("data/kegg_hsa_pathway.rda")
# 
# get_pathway_class(object = kegg_hsa_pathway)
# 
# remain_idx =
#   kegg_hsa_pathway@pathway_class %>%
#   unlist() %>%
#   stringr::str_detect("Disease") %>%
#   `!`() %>%
#   which()
# 
# pathway_database =
#   filter_pathway(object = kegg_hsa_pathway, remain_idx = remain_idx)
# 
# load("data/query_id_kegg.rda")
# 
# kegg_enrichment =
#   enrich_kegg(
#     query_id = query_id,
#     query_type = "compound",
#     id_type = "KEGG",
#     pathway_database = pathway_database,
#     p_cutoff = 0.05,
#     p_adjust_method = "BH",
#     method = "hypergeometric",
#     threads = 5
#   )
# 
# enrich_bar_plot(object = kegg_enrichment)
# enrich_scatter_plot(object = kegg_enrichment)
# enrich_network(object = kegg_enrichment)

enrich_kegg = function(query_id,
                       query_type = c("compound", "gene"),
                       id_type = c("KEGG"),
                       pathway_database,
                       p_cutoff = 0.05,
                       p_adjust_method = c("holm",
                                           "hochberg",
                                           "hommel",
                                           "bonferroni",
                                           "BH",
                                           "BY",
                                           "fdr",
                                           "none"),
                       method = c("hypergeometric", "fisher"),
                       threads = 3) {
  query_type = match.arg(query_type)
  id_type = match.arg(id_type)
  method <- match.arg(method)
  p_adjust_method = match.arg(p_adjust_method)
  
  if (query_type == "compound") {
    if (pathway_database@database_info$source != "KEGG") {
      stop("pathway_database must from KEGG.\n")
    }
    
    remain_idx =
      pathway_database@compound_list %>%
      purrr::map(function(x) {
        nrow(x)
      }) %>%
      unlist() %>%
      `>`(0) %>%
      which()
    
    pathway_database =
      filter_pathway(object = pathway_database, remain_idx = remain_idx)
    
    for (i in 1:length(pathway_database@pathway_id)) {
      pathway_database@compound_list[[i]] =
        pathway_database@compound_list[[i]] %>%
        dplyr::filter(!is.na(KEGG.ID)) %>%
        dplyr::filter(KEGG.ID != "")
    }
    
    ##remove pathways without compound
    remain_idx =
      pathway_database@compound_list %>%
      purrr::map(function(x) {
        nrow(x)
      }) %>%
      unlist() %>%
      `>`(0) %>%
      which()
    
    pathway_database =
      filter_pathway(object = pathway_database, remain_idx = remain_idx)
    
    if (length(pathway_database@pathway_id) == 0) {
      stop("Pathway database length is zero.\n")
    } else{
      message(crayon::green(length(pathway_database@describtion), "pathways."))
    }
    
    database = pathway_database@compound_list
    names(database) = pathway_database@pathway_id
    
    database =
      database %>%
      purrr::map(function(x) {
        x$KEGG.ID
      })
    
    for(idx in 1:length(pathway_database@describtion)){
      if(is.null(pathway_database@describtion[[idx]])){
        pathway_database@describtion[[idx]] = NA
      }
      pathway_database@describtion[[idx]] = 
        paste(pathway_database@describtion[[idx]], collapse = "{}")
    }
    
    pathway_info = data.frame(
      pathway_id = pathway_database@pathway_id,
      pathway_name = pathway_database@pathway_name,
      describtion = unlist(pathway_database@describtion),
      pathway_class = unlist(pathway_database@pathway_class),
      stringsAsFactors = FALSE
    )
  }
  
  all_id <-
    database %>%
    unlist() %>%
    unique() %>%
    unname() %>%
    as.character()
  
  ##remove the ID which is not in the all_id
  query_id <- query_id[which(is.element(query_id, all_id))]
  
  if (length(query_id) == 0) {
    return(NULL)
  }
  
  sig_id <- as.character(query_id)
  
  num_all <- length(all_id)
  num_sig <- length(sig_id)
  
  # --------------------------------------------------------------
  #Generating label matrix for detected metabolites
  # --------------------------------------------------------------
  
  all_matrix <-
    set_label(query_id = all_id,
              database = database,
              threads = threads)
  
  # delete metabolite set
  all_matrix2 <-
    all_matrix[, colSums(all_matrix) != 0, drop = FALSE]
  
  # error handling
  if (ncol(all_matrix2) < 2) {
    # stop function
    return(NULL)
  }
  
  # --------------------------------------------------------------
  #Generating label matrix for significant metabolites
  # --------------------------------------------------------------
  
  sig_matrix <-
    set_label(query_id = sig_id,
              database = database,
              threads = threads)
  
  sig_matrix <-
    sig_matrix[, colSums(all_matrix) != 0, drop = FALSE]
  
  # -------------------------------
  #Calculating  ORA
  # -------------------------------
  #for each pathway
  p_value =
    furrr::future_map(
      .x = 1:ncol(all_matrix2),
      .f = function(i) {
        # ------------------------------------
        #Generating 2~2 table
        # -------------------------------------
        a1 <-
          sum(sig_matrix[, i])# significant and including pathway
        a2 <-
          sum(all_matrix2[, i]) - sum(sig_matrix[, i])# not significant and including pathway
        a3 <-
          length(sig_id) - a1# significant and not including pathway
        a4 <-
          (length(all_id) - length(sig_id)) - a2# not significant and not including pathway
        
        tab <- t(matrix(c(a1, a2, a3, a4), 2))
        if (method == "hypergeometric") {
          phyper(
            q = a1 - 1,
            m = sum(all_matrix2[, i]),
            n = num_all - sum(all_matrix2[, i]),
            k = num_sig,
            lower.tail = FALSE
          )
        } else{
          # ----------------------------------
          # Fisher's exact test
          # ----------------------------------
          check <- tryCatch({
            resfish <- fisher.test(tab, alternative = "greater")
          }, error = function(e) {
            NA
          })
          
          if (class(check) != "htest") {
            return(1)
          } else{
            resfish <- fisher.test(tab, alternative = "greater")
            return(resfish$p.value)
          }
        }
      },
      .progress = TRUE
    ) %>%
    unlist()
  
  # -----------------------
  #q-value
  # -----------------------
  p_value_adjust <- p.adjust(p_value, method = p_adjust_method)
  
  # ----------------------
  #Result
  # ----------------------
  result =
    data.frame(pathway_info, p_value, p_value_adjust)
  
  result$all_id =
    furrr::future_map(
      .x = result$pathway_id,
      .f = function(x) {
        paste(database[[x]], collapse = ";")
      }
    ) %>%
    unlist()
  
  result$all_number =
    furrr::future_map(
      .x = result$pathway_id,
      .f = function(x) {
        length(database[[x]])
      }
    ) %>%
    unlist()
  
  result$mapped_id =
    furrr::future_map(
      .x = result$pathway_id,
      .f = function(x) {
        paste(query_id[query_id %in% database[[x]]], collapse = ";")
      }
    ) %>%
    unlist()
  
  result$mapped_number =
    furrr::future_map(
      .x = result$pathway_id,
      .f = function(x) {
        sum(query_id %in% database[[x]])
      }
    ) %>%
    unlist()
  
  result$mapped_percentage =
    furrr::future_map(
      .x = result$pathway_id,
      .f = function(x) {
        sum(query_id %in% database[[x]]) * 100 / length(database[[x]])
      }
    ) %>%
    unlist()
  
  # result =
  #   result %>%
  #   dplyr::arrange(p_value) %>%
  #   dplyr::filter(p_value <= p_cutoff)
  
  return_result =
    new(
      Class = "enrich_result_class",
      pathway_database = pathway_database@database_info$source,
      pathway_version = pathway_database@database_info$version,
      result = result
    )
  
}



#' @title set_label
#' @description set_label
#' @author Xiaotao Shen
#' \email{shenxt@@stanford.edu}
#' @param query_id query_id
#' @param database database
#' @param threads threads
#' @return  The MSE analysis result.

set_label <- function (query_id,
                       database,
                       threads = parallel::detectCores() - 2) {
  future::plan(strategy = future::multisession, workers = threads)
  return_result =
    furrr::future_map(
      .x = database,
      .f = function(x) {
        temp = match(query_id, x)
        temp[!is.na(temp)] = 1
        temp[is.na(temp)] = 0
        temp
      }
    ) %>%
    do.call(cbind, .) %>%
    as.data.frame()
  
  rownames(return_result) = query_id
  return_result
}