R/quickHClust.R

In TimiRGeN: Time sensitive microRNA-mRNA integration, analysis and network generation tool

#' @title quickHClust
#' @description Plots all the genes found in a particular cluster. The plots
#' will contain the data (gray) and a smoothed line (red).
#' @param filt_df Dataframe from the matrixFilter function.
#' @param miRNA_exp miRNA data from using the diffExpressRes function on miRNA
#' data.
#' @param mRNA_exp mRNA data from using the diffExpressRes function on miRNA
#' data.
#' @param distmeth Dist method for hierarchical clustering. Default is
#' "maximum".
#' @param hclustmeth Hclust method for hierarchical clustering. Default is
#' "ward.D".
#' @param pathwayname Character which is the name of pathway of interest.
#' Default is "Pathway".
#' @param k Integer. Number of clusters.
#' @param cluster Integer. Which cluster to look into? Default is 1.
#' @return Time course plots of each gene found in the cluster of interest,
#' from the pathway of interest.
#' @export
#' @importFrom stats cutree
#' @importFrom dplyr inner_join filter
#' @importFrom ggplot2 geom_line geom_smooth facet_wrap
#' @usage quickHClust(filt_df, miRNA_exp, mRNA_exp, distmeth,hclustmeth,
#' pathwayname, k, cluster)
#' @examples
#' library(org.Mm.eg.db)
#' miR <- mm_miR[1:100,]
#' mRNA <- mm_mRNA[1:200,]
#'
#' MAE <- startObject(miR = miR, mRNA = mRNA)
#'
#' MAE <- getIdsMir(MAE, assay(MAE, 1), orgDB = org.Mm.eg.db, 'mmu')
#' MAE <- getIdsMrna(MAE, assay(MAE, 2), "useast", 'mmusculus')
#'
#' MAE <- diffExpressRes(MAE, df = assay(MAE, 1), dataType = 'Log2FC',
#'                      genes_ID = assay(MAE, 3),
#'                       idColumn = 'GENENAME',
#'                       name = "miRNA_log2fc")
#'
#' MAE <- diffExpressRes(MAE, df = assay(MAE, 2), dataType = 'Log2FC',
#'                      genes_ID = assay(MAE, 7),
#'                      idColumn = 'GENENAME',
#'                      name = "mRNA_log2fc")
#'
#' Filt_df <- data.frame(row.names = c("mmu-miR-145a-3p:Adamts15",
#'                                    "mmu-miR-146a-5p:Acy1"),
#'                      corr = c(-0.9191653, 0.7826041),
#'                      miR = c("mmu-miR-145a-3p", "mmu-miR-146a-5p"),
#'                      mRNA = c("Adamts15", "Acy1"),
#'                      miR_Entrez = c(387163, NA),
#'                      mRNA_Entrez = c(235130, 109652),
#'                      TargetScan = c(1, 0),
#'                      miRDB = c(0, 0),
#'                      Predicted_Interactions = c(1, 0),
#'                      miRTarBase = c(0, 1),
#'                      Pred_Fun = c(1, 1))
#'
#' MAE <- matrixFilter(MAE, miningMatrix = Filt_df, negativeOnly = FALSE,
#'                    threshold = 1, predictedOnly = FALSE)
#'
#' quickHClust(filt_df=MAE[[11]], miRNA_exp=MAE[[9]],
#'             mRNA_exp=MAE[[10]], pathwayname = "Test", k = 2, cluster = 1)
quickHClust <- function(filt_df, miRNA_exp, mRNA_exp, distmeth="maximum",
                        hclustmeth = "ward.D", pathwayname = "Pathway", k,
                        cluster = "1"){

  Cluster <- Time <- Expression <- NULL

  Prep <- clustPrep(filt_df, miRNA_exp, mRNA_exp)

  fit <- hClustPrep(filt_df, miRNA_exp, mRNA_exp, distmeth,

                    hclustmeth)

  clustered_data <- cutree(fit, k=k)

  clustered_data_tidy <- as.data.frame(as.table(clustered_data))

  colnames(clustered_data_tidy) <- c("Gene","Cluster")

  clustered_data_tidy$Gene <- as.character(clustered_data_tidy$Gene)

  joined_clusters <- Prep %>%

    inner_join(clustered_data_tidy, by = "Gene")

  JC <- joined_clusters %>% filter(Cluster == cluster)

  suppressWarnings(ggplot(JC, aes(Time, Expression)) +

    geom_line(color="grey", size=1) +

    geom_smooth(method="auto",color="red", se=FALSE, size=1) +

    facet_wrap(~Gene)+

    theme_bw() +

    labs(title= paste0(pathwayname," Genes in Cluster ", cluster),

         x="Time",

         y="Scaled Expression")+

    theme(plot.title=element_text(size=20, face="bold",hjust = 0.5),

          axis.text.x=element_text(size=15),

          axis.text.y=element_text(size=15),

          axis.title.x=element_text(size=20),

          axis.title.y=element_text(size=20))+

    theme(axis.line = element_line(colour = "black"),

          panel.grid.major = element_blank(),

          panel.grid.minor = element_blank(),

          panel.border = element_blank(),

          panel.background = element_blank()))+

    theme(strip.text.x = element_text(size = 12))

}