R/quickTCPred.R

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

#' @title quickTCPred
#' @description Creates a regression plot over the time course, which compares
#' data and a simulation which was predicted using the data. Data is based
#' on the model formula used in the multiReg function and linearRegr function.
#' R.squared and p value are also calculated and pasted into the plot.
#' @param model Linear model which was generated by the linearRegr function.
#' @param reg_df Matrix/assay which which was generated by the multiReg function.
#' Should be in the MAE used during the multiReg function.
#' @return A linear regression plot with data and a simulation.
#' @export
#' @usage quickTCPred(model, reg_df)
#' @importFrom ggplot2 geom_point
#' @importFrom stats predict
#' @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)
#'
#' MAE <- multiReg(MAE = MAE, gene_interest = "Adamts15",
#'                 mRNAreg =TRUE, filt_df=MAE[[11]], miRNA_exp=MAE[[9]],
#'                 mRNA_exp=MAE[[10]])
#'
#' model1 <- linearRegr(mreg = MAE[[12]], colselect =2, colpair =3)
#' 
#' summary(model1$regression)
#'
#' quickTCPred(model = model1, reg_df = MAE[[12]])
quickTCPred <- function(model, reg_df){

  if (missing(model)) stop('model is missing. Add linear regression model generated by the linearRegr function.')

  if (missing(reg_df)) stop('reg_df is missing. Add matrix/assay generated by the multiReg function.')

  Time <- Values <- Simulation <- NULL

  S <- summary(model$regression)

  Rs <- round(S$r.squared, digits = 2)

  Pval <- round(getP(model$regression), digits = 2)

  Gene <- colnames(reg_df)[2]

  Prediction <- predict(model$regression)

  X <- cbind(Data = reg_df[,c(2)], Prediction)

  rownames(X) <- as.integer(gsub(rownames(X), pattern = "[^0-9.-]",

                                 replacement = ""))

  X <- as.data.frame(X)

  Melted <- melt(as.matrix(X), varnames = c("Time", "Simulation"))

  names(Melted)[[3]] <- "Values"

  ggplot(Melted, aes(x = Time, y = Values, group = Simulation,

                     color = Simulation)) +

    geom_point(data = ~ subset(., Simulation == paste("Data")), size =5) +

    geom_line(data = ~ subset(., Simulation == paste("Prediction")),

              size =2, linetype = "dashed") +

    scale_colour_manual(values=c("Red", "Blue"))+

    theme_classic()+

    labs(title= paste0(Gene,":Interactions Time Series Regression"),

         x="Time",

         y="Expression",

         subtitle=paste0("R.sqrd = ", Rs, " |", " Pval = ", Pval))+

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

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

          axis.text.y=element_text(size=20),

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

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

          legend.text=element_text(size=15))+

    theme(plot.subtitle=element_text(size=20, hjust=1.2,

                                     face="italic", color="black"))

}