R/doSummaryPlot.R

Defines functions doSummaryPlot

Documented in doSummaryPlot

#' @importFrom gridExtra grid.arrange
#' @importFrom ggplot2 ggsave
#'
NULL

#' Wrapper function to generate PCA plot and RSD statistics plot
#'
#' @param Data Data frame.
#' @param classes Vector of class labels.
#' @param blank Label used for blank samples, if set to NULL no samples will be
#' removed
#' @param PQN Can be set to T or F, to perform PQN normalisation
#' @param mv_impute T or F, indicates if missing value imputation has to be
#' carried
#' @param glogScaling T or F, applie glog transformation to the given data
#' @param scale Perform UV scaling on data
#' @param qc_label Label used for QC samples. If set to NULL, assumes that no
#' QC samples are present in data set
#' @param ignorelabel Label for samples which should be excluded from processed
#' data
#' @param output File name for pdf output
#' @param labels Can be set to "QC" to label only QC samples. "none" to no
#' include labels. If set to any other value will use column names of Data.
#' @param qc_shape Shape symbol to use for QC samples
#' @param base_size Ggplot font size
#' @param pccomp PCA components to plot
#' @param plot If set to T will output pdf file, otherwise ggplot object
#' @param plotTitle Title to use for plot output
#' @return Summary plot as ggplot object or directly to the pdf file.
#' @export


doSummaryPlot <- function(Data, classes, plotTitle="PCA", blank="BLANK",
  PQN=FALSE, mv_impute=TRUE, glogScaling=TRUE, scale=TRUE, qc_label="QC",
  ignorelabel="Removed", output="PCA_plot.pdf", labels="QC",
  qc_shape=17L, base_size=10L, pccomp=c(1L, 2L), plot=TRUE) {
  Nbatches <- NULL

  if (!is.data.frame(Data) & is.list(Data)) {

    Nbatches <- length(Data)
  }

  if (is.data.frame(Data) | is.matrix(Data)) {

    Nbatches <- 1L
    Data <- list(Data)
    classes <- list(classes)
    plotTitle <- list(plotTitle)
  }

  if (is.null(Nbatches)) {

    stop("Input data should be data frame object, matrix or list of data frames
         if you want to process more than one batch \n")
  }

  # RSD plot is always plotted
  plots <- vector("list", Nbatches * 2L)
  ind <- seq(1L, length(plots), 2L)

  for (nb in 1L:Nbatches) {

    sData <- data.frame(Data[[nb]])
    sclass <- classes[[nb]]
    prepData <- prepareData(Data=sData, classes=sclass, blank=blank,
      qc_label=qc_label, PQN=PQN, mv_impute=mv_impute, glogScaling=glogScaling,
      ignorelabel="Removed")

    hits4 <- which(names(prepData$RSD) == qc_label)

    if (length(hits4) > 0L) {

      QC_RSD <- prepData$RSD[[hits4]]
      QC_RSD_sum <- summary(QC_RSD)
      subt <- paste("QC RSD%:", paste(paste(names(QC_RSD_sum),
        round(QC_RSD_sum, 1L), sep=" "), collapse=", "))
      subt2 <- paste0(" QC RSD is <30% in ",
        round(length(which(QC_RSD <= 30.0)) /
        length(QC_RSD) * 100.0, 0L), "% (", length(which(QC_RSD <= 30.0)), "/",
        length(QC_RSD), ") of features")

    } else {

      subt <- NULL
      subt2 <- NULL
    }

    ## PCA
    plots[[ind[nb]]] <- doPCA(Data=prepData$Data, classes=prepData$classes,
      plotTitle=plotTitle[[nb]],
      scale=scale, labels=labels, qc_shape=qc_shape, base_size=base_size,
      pccomp=pccomp, subtitle=subt, qc_label=qc_label, PQN=PQN,
      mv_impute=mv_impute, glogScaling=glogScaling)

      # RSD per class
    plots[[ind[nb] + 1L]] <- do_variability_plot(list_object=prepData$RSD,
      plotTitle="", base_size=12L, subtitle=subt2)
  }

  if (plot == TRUE) {

    plots <- gridExtra::grid.arrange(grobs=plots, ncol=2L)
    ggplot2::ggsave(filename=output, plot=plots, width=180L,
      height=90L * Nbatches, units="mm")

  } else {

    return(plots)
  }
}
computational-metabolomics/qcrms documentation built on March 25, 2020, 3:27 p.m.