R/plot_variation.R

In SWATH2stats: Transform and Filter SWATH Data for Statistical Packages

#' Plots the coefficient of variation for different replicates.
#'
#' This function plots the coefficient of variation within replicates for a
#' given value. If decoys are present these are removed before plotting.
#'
#' @param data Data frame that is produced by the OpenSWATH/pyProphet workflow.
#' @param column.values  Indicates the columns for which the variation is
#'   assessed. This can be the Intensity or Signal, but also the retention
#'   time.
#' @param comparison The comparison for assessing the variability. Default is to
#'   assess the variability per transition_group_id and Condition over the
#'   different Replicates. Comparison is performed using the dcast() function of
#'   the reshape2 package.
#' @param fun_aggregate If for the comparison values have to be aggregated one
#'   needs to provide the function here.
#' @param label  Option to print value of median cv.
#' @param title Title of plot. Default: "cv across conditions"
#' @param boxplot   Logical. If boxplot should be plotted. Default: TRUE
#' @param ... further arguments passed to method.
#' @return Returns a list with the data and calculated cv and a table that
#'   summarizes the mean, median and mode cv per Condition (if Condition is
#'   contained in the comparison). In addition it plots in Rconsole a violin
#'   plot with the observed coefficient of variations.
#' @author Peter Blattmann
#' @examples{
#'  data("OpenSWATH_data", package="SWATH2stats")
#'  data("Study_design", package="SWATH2stats")
#'  data <- sample_annotation(OpenSWATH_data, Study_design)
#'  var_summary <- plot_variation(data)
#'  }
#' @importFrom reshape2 dcast
#' @importFrom stats sd na.omit aggregate density
#' @importFrom ggplot2 stat_summary aes_string geom_violin theme labs
#' @export
plot_variation <- function(data, 
                           column.values = "Intensity", 
                           comparison = transition_group_id + Condition ~ BioReplicate, 
                           fun_aggregate = NULL, 
                           label = FALSE, 
                           title = "cv across conditions",
                           boxplot = TRUE, ...) {
    if (sum(colnames(data) == "decoy") == 1) {
        data <- data[data$decoy == 0, ]
    }
    data.c <- dcast(data, comparison, value.var = column.values, 
                    fun.aggregate = fun_aggregate)
    data.c[data.c == 0] <- NA

    n_vars <- length(all.vars(comparison))

    data.sd <- apply(data.c[, n_vars:dim(data.c)[2]], 1, 
                     function(x) sd(x, na.rm = TRUE))
    data.mean <- apply(data.c[, n_vars:dim(data.c)[2]], 1, 
                       function(x) mean(x, na.rm = TRUE))
    data.c$cv <- data.sd/data.mean
    mean.cv <- mean(data.c$cv, na.rm = TRUE)
    median.cv <- median(data.c$cv, na.rm = TRUE)

    data.cv <- data.c[, c(colnames(data.c)[2], "cv")]

    p <- (ggplot(na.omit(data.cv), aes_string(x = colnames(data.cv)[1], y = "cv")) +
        geom_violin(scale = "area") + theme(axis.text.x = element_text(size = 8,
        angle = 90, hjust = 1, vjust = 0.5)) + labs(title = title))

    if (isTRUE(label)) {
        p <- p + stat_summary(fun.data = function(x) data.frame(y = max(x) * 0.75,
            label = paste("median cv:\n", signif(median(x, na.rm = TRUE), digits = 2))),
            geom = "text")
    }

    if (isTRUE(boxplot)) {
        p <- p + geom_boxplot(width = 0.1, outlier.shape = NA)
    }

    print(p)
    if ("Condition" %in% colnames(data.c)) {
        median <- aggregate(data.c[, "cv"], by = list(data.c$Condition), FUN = function(x) median(x,
            na.rm = TRUE))
        colnames(median) <- c("Condition", "median_cv")
        mean <- aggregate(data.c[, "cv"], by = list(data.c$Condition), FUN = function(x) mean(x,
            na.rm = TRUE))
        colnames(mean) <- c("Condition", "mean_cv")
        mode <- aggregate(data.c[, "cv"], by = list(data.c$Condition), FUN = function(x) {
            d <- density(x, na.rm = TRUE)
            i <- which.max(d$y)
            return(d$x[i])
        })
        colnames(mode) <- c("Condition", "mode_cv")

        cv_table <- merge(mode, merge(mean, median, by = "Condition"), by = "Condition")

        return(list(data.c, cv_table))
    }
    return(data.c)
}