R/enrichR_functions.R

Defines functions plot_gsea test_gsea

Documented in plot_gsea test_gsea

#' Gene Set Enrichment Analysis
#'
#' \code{test_gsea} tests for enriched gene sets
#' in the differentially enriched proteins.
#' This can be done independently for the different contrasts.
#'
#' @param dep SummarizedExperiment,
#' Data object for which differentially enriched proteins are annotated
#' (output from \code{\link{test_diff}()} and \code{\link{add_rejections}()}).
#' @param databases Character,
#' Databases to search for gene set enrichment.
#' See http://amp.pharm.mssm.edu/Enrichr/ for available databases.
#' @param contrasts Logical(1),
#' Whether or not to perform the gene set enrichment analysis
#' independently for the different contrasts.
#' @return A data.frame with enrichment terms (generated by \code{\link[enrichR]{enrichr}})
#' @examples
#' # Load example
#' data <- UbiLength
#' data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",]
#' data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";")
#'
#' # Make SummarizedExperiment
#' columns <- grep("LFQ.", colnames(data_unique))
#' exp_design <- UbiLength_ExpDesign
#' se <- make_se(data_unique, columns, exp_design)
#'
#' # Filter, normalize and impute missing values
#' filt <- filter_missval(se, thr = 0)
#' norm <- normalize_vsn(filt)
#' imputed <- impute(norm, fun = "MinProb", q = 0.01)
#'
#' # Test for differentially expressed proteins
#' diff <- diff <- test_diff(imputed, "control", "Ctrl")
#' dep <- add_rejections(diff, alpha = 0.05, lfc = 1)
#'
#' \dontrun{
#'
#' # Test enrichments
#' gsea_results_per_contrast <- test_gsea(dep)
#' gsea_results <- test_gsea(dep, contrasts = FALSE)
#'
#' gsea_kegg <- test_gsea(dep, databases = "KEGG_2016")
#'
#' }
#' @export
test_gsea <- function(dep,
                      databases = c("GO_Molecular_Function_2017b",
                                    "GO_Cellular_Component_2017b",
                                    "GO_Biological_Process_2017b"),
                      contrasts = TRUE) {
  # Show error if inputs are not the required classes
  assertthat::assert_that(inherits(dep, "SummarizedExperiment"),
                          is.character(databases),
                          is.logical(contrasts),
                          length(contrasts) == 1)

  # Check whether 'enrichR' is installed
  if(!"enrichR" %in% rownames(installed.packages())) {
    stop("test_enrichR() requires the 'enrichR' package",
         "\nTo install the package run: install.packages('enrichR')")
  }

  row_data <- rowData(dep, use.names = FALSE)
  # Show error if inputs do not contain required columns
  if(any(!c("name", "ID") %in% colnames(row_data))) {
    stop("'name' and/or 'ID' columns are not present in '",
         deparse(substitute(dep)),
         "'\nRun make_unique() and make_se() to obtain the required columns",
         call. = FALSE)
  }
  if(length(grep("_p.adj|_diff", colnames(row_data))) < 1) {
    stop("'[contrast]_diff' and/or '[contrast]_p.adj' columns are not present in '",
         deparse(substitute(dep)),
         "'\nRun test_diff() to obtain the required columns",
         call. = FALSE)
  }

  # Check for valid databases
  libraries <- enrichR::listEnrichrDbs()$libraryName
  if(all(!databases %in% libraries)) {
    stop("Please run `test_gsea()` with valid databases as argument",
         "\nSee http://amp.pharm.mssm.edu/Enrichr/ for available databases")
  }
  if(any(!databases %in% libraries)) {
    databases <- databases[databases %in% libraries]
    message("Not all databases found",
            "\nSearching the following databases: '",
            paste0(databases, collapse = "', '"), "'")
  }

  # Run background list
  message("Background")
  background <- gsub("[.].*", "", row_data$name)
  background_enriched <- enrichR::enrichr(background, databases)
  df_background <- NULL
  for(database in databases) {
    temp <- background_enriched[database][[1]] %>%
      mutate(var = database)
    df_background <- rbind(df_background, temp)
  }
  df_background$contrast <- "background"
  df_background$n <- length(background)

  OUT <- df_background %>%
    mutate(bg_IN = as.numeric(gsub("/.*", "", Overlap)),
           bg_OUT = n - bg_IN) %>%
    select(Term, bg_IN, bg_OUT)

  if(contrasts) {
    # Get gene symbols
    df <- row_data %>%
      as.data.frame() %>%
      select(name, ends_with("_significant")) %>%
      mutate(name = gsub("[.].*", "", name))

    # Run enrichR for every contrast
    df_enrich <- NULL
    for(contrast in colnames(df[2:ncol(df)])) {
      message(gsub("_significant", "", contrast))
      significant <- df[df[[contrast]],]
      genes <- significant$name
      enriched <- enrichR::enrichr(genes, databases)

      # Tidy output
      contrast_enrich <- NULL
      for(database in databases) {
        temp <- enriched[database][[1]] %>%
          mutate(var = database)
        contrast_enrich <- rbind(contrast_enrich, temp)
      }
      contrast_enrich$contrast <- contrast
      contrast_enrich$n <- length(genes)

      # Background correction
      cat("Background correction... ")
      contrast_enrich <- contrast_enrich %>%
        mutate(IN = as.numeric(gsub("/.*", "", Overlap)),
               OUT = n - IN) %>%
        select(-n) %>%
        left_join(OUT, by = "Term") %>%
        mutate(log_odds = log2((IN * bg_OUT) / (OUT * bg_IN)))
      cat("Done.")

      df_enrich <- rbind(df_enrich, contrast_enrich) %>%
        mutate(contrast = gsub("_significant", "", contrast))
    }
  } else {
    # Get gene symbols
    significant <- row_data %>%
      as.data.frame() %>%
      select(name, significant) %>%
      filter(significant) %>%
      mutate(name = gsub("[.].*", "", name))

    # Run enrichR
    genes <- significant$name
    enriched <- enrichR::enrichr(genes, databases)

    # Tidy output
    df_enrich <- NULL
    for(database in databases) {
      temp <- enriched[database][[1]] %>%
        mutate(var = database)
      df_enrich <- rbind(df_enrich, temp)
    }
    df_enrich$contrast <- "significant"
    df_enrich$n <- length(genes)

    # Background correction
    cat("Background correction... ")
    df_enrich <- df_enrich %>%
      mutate(IN = as.numeric(gsub("/.*", "", Overlap)),
             OUT = n - IN) %>%
      select(-n) %>%
      left_join(OUT, by = "Term") %>%
      mutate(log_odds = log2((IN * bg_OUT) / (OUT * bg_IN)))
    cat("Done.")
  }

  return(df_enrich)
}

#' Plot enriched Gene Sets
#'
#' \code{plot_gsea} plots enriched gene sets
#' from Gene Set Enrichment Analysis.
#'
#' @param gsea_results Data.frame,
#' Gene Set Enrichment Analysis results object.
#' (output from \code{\link{test_gsea}()}).
#' @param number Numeric(1),
#' Sets the number of enriched terms per contrast to be plotted.
#' @param alpha Numeric(1),
#' Sets the threshold for the adjusted P value.
#' @param contrasts Character,
#' Specifies the contrast(s) to plot.
#' If 'NULL' all contrasts will be plotted.
#' @param databases Character,
#' Specifies the database(s) to plot.
#' If 'NULL' all databases will be plotted.
#' @param nrow Numeric(1),
#' Sets the number of rows for the plot.
#' @param term_size Numeric(1),
#' Sets the text size of the terms.
#' @return A barplot of the enriched terms
#' (generated by \code{\link[ggplot2]{ggplot}}).
#' @examples
#' # Load example
#' data <- UbiLength
#' data <- data[data$Reverse != "+" & data$Potential.contaminant != "+",]
#' data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";")
#'
#' # Make SummarizedExperiment
#' columns <- grep("LFQ.", colnames(data_unique))
#' exp_design <- UbiLength_ExpDesign
#' se <- make_se(data_unique, columns, exp_design)
#'
#' # Filter, normalize and impute missing values
#' filt <- filter_missval(se, thr = 0)
#' norm <- normalize_vsn(filt)
#' imputed <- impute(norm, fun = "MinProb", q = 0.01)
#'
#' # Test for differentially expressed proteins
#' diff <- diff <- test_diff(imputed, "control", "Ctrl")
#' dep <- add_rejections(diff, alpha = 0.05, lfc = 1)
#'
#' \dontrun{
#'
#' # Test enrichments
#' gsea_results <- test_gsea(dep)
#' plot_gsea(gsea_results)
#'
#' }
#' @export
plot_gsea <- function(gsea_results, number = 10, alpha = 0.05,
                      contrasts = NULL, databases = NULL,
                      nrow = 1,term_size = 8) {
  assertthat::assert_that(is.data.frame(gsea_results),
                          is.numeric(number),
                          length(number) == 1,
                          is.numeric(alpha),
                          length(alpha) == 1,
                          is.numeric(term_size),
                          length(term_size) == 1,
                          is.numeric(nrow),
                          length(nrow) == 1)

  # Check gsea_results object
  if(any(!c("Term", "var",
            "contrast","Adjusted.P.value")
         %in% colnames(gsea_results))) {
    stop("'", deparse(substitute(gsea_results)),
         "' does not contain the required columns",
         "\nRun test_enrichment() to obtain the required columns",
         call. = FALSE)
  }

  if(!is.null(contrasts)) {
    assertthat::assert_that(is.character(contrasts))

    valid_contrasts <- unique(gsea_results$contrast)

    if(!all(contrasts %in% valid_contrasts)) {
      valid_cntrsts_msg <- paste0("Valid contrasts are: '",
                                  paste0(valid_contrasts, collapse = "', '"),
                                  "'")
      stop("Not a valid contrast, please run `plot_gsea()`",
           "with a valid contrast as argument\n",
           valid_cntrsts_msg,
           call. = FALSE)
    }
    if(!any(contrasts %in% valid_contrasts)) {
      contrasts <- contrasts[contrasts %in% valid_contrasts]
      message("Not all contrasts found",
              "\nPlotting the following contrasts: '",
              paste0(contrasts, collapse = "', '"), "'")
    }

    gsea_results <- filter(gsea_results, contrast %in% contrasts)
  }
  if(!is.null(databases)) {
    assertthat::assert_that(is.character(databases))

    valid_databases <- unique(gsea_results$var)

    if(all(!databases %in% valid_databases)) {
      valid_cntrsts_msg <- paste0("Valid databases are: '",
                                  paste0(valid_databases, collapse = "', '"),
                                  "'")
      stop("Not a valid database, please run `plot_gsea()`",
           "with valid databases as argument\n",
           valid_cntrsts_msg,
           call. = FALSE)
    }
    if(any(!databases %in% valid_databases)) {
      databases <- databases[databases %in% valid_databases]
      message("Not all databases found",
              "\nPlotting the following databases: '",
              paste0(databases, collapse = "', '"), "'")
    }

    gsea_results <- filter(gsea_results, var %in% databases)
  }

  # Get top enriched gene sets
  terms <- gsea_results %>%
    group_by(contrast, var) %>%
    filter(Adjusted.P.value <= alpha) %>%
    arrange(Adjusted.P.value) %>%
    dplyr::slice(seq_len(number)) %>%
    .$Term
  subset <- gsea_results %>%
    filter(Term %in% terms) %>%
    arrange(var, Adjusted.P.value)

  subset$Term <- parse_factor(subset$Term, levels = unique(subset$Term))
  subset$var <- parse_factor(subset$var, levels = unique(subset$var))

  # Plot top enriched gene sets
  ggplot(subset, aes(Term,
                     log_odds)) +
    #geom_tile(aes(fill = var)) +
    geom_col(aes(fill = -log10(Adjusted.P.value))) +
    facet_wrap(~contrast, nrow = nrow) +
    coord_flip() +
    labs(y = "Log2 odds ratio (versus current background)",
         fill = "-Log10 adjusted P value") +
    theme_DEP1() +
    theme(legend.position = "top",
          axis.text.y = element_text(size = term_size),
          legend.text = element_text(size = 9))
}
arnesmits/DEP documentation built on Dec. 11, 2018, 7:39 p.m.