R/enrichfindP.R

In mrbakhsh/HPiP: Host-Pathogen Interaction Prediction

    #' enrichfindP
    #' @title Functional Enrichment Analysis for Pathogen Interactors in
    #' the High-Confidence Network.
    #' @param ppi A data.frame containing pathogen proteins in the
    #' first column and host proteins in the second column.
    #' @param threshold Custom p-value threshold for significance.
    #' @param sources A vector of data sources to use.
    #' See \code{\link[gprofiler2]{gost}} for more details.
    #' @param p.corrction.method The algorithm used for multiple testing
    #' correction;defaults to 'bonferroni'.
    #' See \code{\link[gprofiler2]{gost}} for more details.
    #' @param org An organism name;defaults to 'hsapiens'.
    #' See \code{\link[gprofiler2]{gost}} for more details.
    #' @return A data.frame with the enrichment analysis results.
    #' @seealso See \code{\link{enrichplot}} for plotting enrichment analysis.
    #' @author Matineh Rahmatbakhsh, \email{matinerb.94@gmail.com}
    #' @importFrom purrr map_df
    #' @importFrom stats aggregate
    #' @importFrom stringr str_split
    #' @description This function uses \code{\link[gprofiler2]{gost}} function
    #' in \code{gprofiler2} package to perfrom functional enrichment analysis
    #' for pathogen interactors in the high-confidence network.
    #' @export
    #' @examples
    #' data('predicted_PPIs')
    #' #perform enrichment
    #' enrich.df <- enrichfindP(predicted_PPIs,
    #' threshold = 0.05,
    #' sources = c("GO", "KEGG"),
    #' p.corrction.method = "bonferroni",
    #' org = "hsapiens")

    enrichfindP <-
      function(ppi, threshold = 0.05,
               sources = c("GO", "KEGG"),
               p.corrction.method = "bonferroni",
               org = "hsapiens") {
        p_value <- NULL
        ppi <- as.data.frame(ppi)

        agg.data <-
          aggregate(list(ppi[, 2]),
            by = list(ppi[, 1]),
            FUN = function(x) {
              paste(unique(x),
                collapse = ";"
              )
            }
          )
        colnames(agg.data)[2] <- "interactors"
        agg.data$interactors <-
          vapply(lapply(strsplit(agg.data$interactors, ";"), unique),
            paste, character(1L),
            collapse = ";"
          )


        indcpx <-
          str_split(agg.data$interactors, ";")
        names(indcpx) <- agg.data$Group.1

        annotCov <- lapply(indcpx, function(x) {
          gprofiler2::gost(x,
            significant = TRUE,
            exclude_iea = TRUE,
            evcodes = TRUE,
            user_threshold = threshold,
            sources = sources,
            correction_method = p.corrction.method,
            organism = org
          )
        })

        df <-
          lapply(annotCov, function(x) as.data.frame(x[[1]]))
        ans <-
          map_df(df, ~ as.data.frame(.x), .id = "id")

        ans <-
          arrange(ans, p_value)

        return(ans)
      }