#' @title Make Boxplot from Comparison Matrix
#' @description
#' From a user-inputted value, finds the mean of
#' that value for each sample, then creates a boxplot
#' comparing the values for each subtype.
#' @param compare.matrix The comparison matrix generated from
#' the compMatrix() function
#' @param value The value to be graphed in the boxplot. Possible
#' values are 'read', 'pdr', 'meth', 'epipoly', and 'shannon'
#' @param type A dataframe containing the type information
#' for the samples in the comparison matrix. The row names should
#' be the names of the samples and there should be one column
#' containing the type information for each sample.
#' @param box.colors A vector of colors to be used as the fill
#' color for each boxplot. If not entered, the default colors
#' of ggplot are used. (default: NULL)
#' @param add.points A boolean stating if the individual points for
#' each sample mean should be displayed over the box plots
#' (default: FALSE)
#' @param points.colors A vector of colors to be used as the color
#' of the individual points for each sample mean. One color is used
#' per subtype. (default: NULL)
#' @param pdf.height An integer representing the height (in inches)
#' of the outputted boxplot pdf file (default: 10)
#' @param pdf.width An integer representing the width (in inches) of
#' the outputted boxplot pdf file (default: 10)
#' @param sve A boolean to save the plot (default: FALSE)
#' @return a data frame containing the mean epigenetic heterogeneity
#' for each sample
#' @examples
#' comp.Matrix<-data.frame(
#' p1=c(0.6,0.3,0.5,0.5,0.5,0.6,0.45,0.57,0.45,0.63,0.58,0.67,0.5,0.42,0.67),
#' p2=c(0.62,0.63,0.55,0.75,0.84,0.58,1,0.33,1,0.97,0.57,0.68,0.73,0.72,0.82),
#' p3=c(0.72,0.53,0.62,0.69,0.37,0.85,1,0.63,0.87,0.87,0.82,0.81,0.79,
#' 0.62,0.68),
#' N1=c(0.15,0.24,0.15,0.26,0.34,0.32,0.23,0.14,0.26,0.32,0.12,0.16,0.31,
#' 0.24,0.32),
#' N2=c(0.32,0.26,0.16,0.36,0.25,0.37,0.12,0.16,0.41,0.47,0.13,0.52,0.42,
#' 0.41,0.23),
#' N3=c(0.21,0.16,0.32,0.16,0.36,0.27,0.24,0.26,0.11,0.27,0.39,0.5,0.4,
#' 0.31,0.33),
#' type=rep(c("pdr","epipoly","shannon"),c(5,5,5)),
#' location=rep(c("chr22-327:350:361:364","chr22-755:761:771:773",
#' "chr22-761:771:773:781","chr22-821:837:844:849","chr22-838:845:850:858"),
#' 3),stringsAsFactors =FALSE )
#'
#' subtype <- data.frame(Type= c(rep('CEBPA_sil', 3), rep('Normal', 3)),
#' row.names <- colnames(comp.Matrix)[1:6],stringsAsFactors = FALSE)
#'
#' epiBox(compare.matrix = comp.Matrix, value = 'epipoly',
#' type <- subtype, box.colors = NULL, add.points = FALSE,
#' points.colors <- NULL, pdf.height = 10, pdf.width = 10,
#' sve = FALSE)
#'
#' @export
epiBox <- function(compare.matrix, value, type, box.colors = NULL,
add.points = FALSE, points.colors = NULL, pdf.height = 10,
pdf.width = 10, sve = FALSE) {
values = c("read", "pdr", "meth", "epipoly", "shannon", "myValues")
if (!(value %in% values)) {
stop("Invalid value '", value, "': Possible values are 'read',
'pdr', 'meth', 'epipoly', 'myValues' or 'shannon'")
}
location.col <- length(compare.matrix)
type.col <- location.col - 1
value.matrix <- compare.matrix[compare.matrix$type == value,
c(-type.col, -location.col)]
mean.value <- data.frame(colMeans(value.matrix,na.rm=TRUE))
mean.value <- merge(type, mean.value, by = 0, all = TRUE)
mean.value <- mean.value[, -1]
y.axis <- paste0("Mean of ", value)
title <- paste0("Mean ", value)
curTheme <- theme(panel.background = element_rect(fill = "white",
colour = "darkgrey"), plot.background = element_rect(fill = "white",
colour = "white"), strip.text = element_text(size = 8,
colour = "black"), strip.background = element_rect(fill = "white",
colour = "black"))
value.plot <- ggplot(mean.value, aes(x = mean.value[,1],
y = mean.value[, 2], fill = mean.value[, 1])) + geom_boxplot() +
ggtitle(title) + scale_x_discrete(name = "Type") +
scale_y_continuous(name = y.axis) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
theme(legend.position = "none") +
curTheme
if (!is.null(box.colors)) {
value.plot <- value.plot + scale_fill_manual(values = box.colors)
}
if (add.points) {
if (is.null(points.colors)) {
value.plot <- value.plot + geom_point(position = "jitter",
size = 1)
} else {
value.plot <- value.plot +
geom_point(aes(colour = factor(mean.value[, 1])), size = 1,
position = "jitter") +
scale_color_manual(values = points.colors)
}
}
if (sve) {
ggsave(paste0(value, "_boxplot.pdf"), plot = value.plot,
height = pdf.height, width = pdf.width)
} else {
value.plot
}
return(mean.value)
}
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