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#' @title Plot Relative Expression Levels
#' @description
#' This function computes for each age category the corresponding relative expression profile.
#'
#' For each age category the corresponding relative expression profile is being computed as follows:
#'
#' \deqn{f_js = ( e_js - e_j min ) / ( e_j max - e_j min )}
#'
#' where \eqn{e_j min} and \eqn{e_j max} denote the minimum/maximum \code{\link{mean}} expression level
#' of phylostratum j over developmental stages s. This linear transformation corresponds to
#' a shift by \eqn{e_j min} and a subsequent shrinkage by \eqn{e_j max - e_j min}.
#' As a result, the relative expression level \eqn{f_js} of developmental stage s
#' with minimum \eqn{e_js} is 0, the relative expression level \eqn{f_js} of the developmental
#' stage s with maximum \eqn{e_js} is 1, and the relative expression levels \eqn{f_js} of
#' all other stages s range between 0 and 1, accordingly.
#' @param ExpressionSet a standard PhyloExpressionSet or DivergenceExpressionSet object.
#' @param Groups a list containing the age categories for which mean expression levels shall be drawn.
#' For ex. evolutionary users can compare old phylostrata: PS1-3 (Class 1) and evolutionary young phylostrata: PS4-12 (Class 2).
#' In this example, the list could be assigned as, \code{Groups = list(c(1:3), c(4:12))}.
#' The group options is limited to 2 Groups.
#' @param modules a list storing three elements for specifying the modules: early, mid, and late.
#' Each element expects a numeric vector specifying the developmental stages
#' or experiments that correspond to each module. For example,
#' \code{module} = \code{list(early = 1:2, mid = 3:5, late = 6:7)} devides a dataset storing seven developmental stages into 3 modules. Default is \code{modules = NULL}.
#' But if specified, a shaded are will be drawn to illustrate stages corresponding to the mid module.
#' @param legendName a character string specifying the legend title.
#' @param xlab label of x-axis.
#' @param ylab label of y-axis.
#' @param main main text.
#' @param y.ticks number of ticks that shall be drawn on the y-axis.
#' @param adjust.range logical indicating whether or not the y-axis scale shall be adjusted to the same range in case two groups are specified. Default is \code{adjust.range = TRUE}.
#' @param alpha transparency of the shaded area (between [0,1]). Default is \code{alpha = 0.1}.
#' @param ... place holder for old version of PlotRE that was based on base graphics instead of ggplot2.
#' @details Studying the relative expression profiles of each phylostratum or divergence-stratum enables the detection
#' of common gene expression patterns shared by several phylostrata or divergence-strata.
#'
#' Finding similar relative expression profiles among phylostrata or divergence-strata suggests that
#' phylostrata or divergence-strata sharing a similar relative expression profile are regulated by similar
#' gene regulatory elements. Hence, these common phylostrata or divergence-strata might govern similar processes in the given developmental time course.
#' @return a plot showing the relative expression profiles of phylostrata or divergence-strata belonging to the same group.
#' @references
#' Domazet-Loso T and Tautz D. 2010. "A phylogenetically based transcriptome age index mirrors ontogenetic divergence patterns". Nature (468): 815-818.
#'
#' Quint M et al. 2012. "A transcriptomic hourglass in plant embryogenesis". Nature (490): 98-101.
#' @author Hajk-Georg Drost
#' @seealso \code{\link{PlotBarRE}}, \code{\link{RE}}, \code{\link{REMatrix}}
#' @examples
#'
#' # read standard phylotranscriptomics data
#' data(PhyloExpressionSetExample)
#' data(DivergenceExpressionSetExample)
#'
#' # example PhyloExpressionSet
#' PlotRE(PhyloExpressionSetExample,
#' Groups = list(c(1:3), c(4:12)),
#' legendName = "PS")
#'
#'
#' # or you can choose any combination of groups
#' PlotRE(PhyloExpressionSetExample,
#' Groups = list(c(1,7,9), c(2:6,8,10:12)),
#' legendName = "PS")
#'
#'
#' # example DivergenceExpressionSet
#' PlotRE(DivergenceExpressionSetExample,
#' Groups = list(c(1:5), c(6:10)),
#' legendName = "DS")
#'
#'
#'
#' @export
PlotRE <- function(ExpressionSet,
Groups = NULL,
modules = NULL,
legendName = "age",
xlab = "Ontogeny",
ylab = "Relative Expression Level",
main = "",
y.ticks = 10,
adjust.range = TRUE,
alpha = 0.008, ...)
{
ExpressionSet <- as.data.frame(ExpressionSet)
is.ExpressionSet(ExpressionSet)
stage <- expr <- age <- NULL
if(is.null(Groups))
stop("Your Groups list does not store any items.", call. = FALSE)
### getting the PS names available in the given expression set
age_names <- as.character(names(table(ExpressionSet[ , 1])))
# test whether all group elements are available in the age vector
# ra <- range(ExpressionSet[ , 1])
if(!all(unlist(Groups) %in% as.numeric(age_names)))
stop("There are items in your Group elements that are not available in the age column of your ExpressionSet.", call. = FALSE)
if (length(Groups) > 2)
stop("Please specify at maximum 2 groups that shall be compared.", call. = FALSE)
### getting the PS names available in the given expression set
nPS <- length(age_names)
nCols <- dim(ExpressionSet)[2]
### define and label the REmatrix that holds the rel. exp. profiles
### for the available PS
MeanValsMatrix <- matrix(NA_real_,nPS,nCols-2)
rownames(MeanValsMatrix) <- age_names
colnames(MeanValsMatrix) <- names(ExpressionSet)[3:nCols]
MeanValsMatrix <- age.apply(ExpressionSet, RE)
mean.age <- data.frame(age = age_names, MeanValsMatrix, stringsAsFactors = FALSE)
mMatrix <- tibble::as_tibble(reshape2::melt(mean.age, id.vars = "age"))
colnames(mMatrix)[2:3] <- c("stage", "expr")
if (length(Groups) == 1) {
p <- ggplot2::ggplot(mMatrix, ggplot2::aes( factor(stage, levels = unique(stage)), expr, group = age, fill = factor(age, levels = age_names))) +
ggplot2::geom_line(ggplot2::aes(color = factor(age, levels = age_names)), size = 3) +
ggplot2::labs(x = xlab, y = ylab, title = main, colour = legendName) +
ggplot2::theme_minimal() +
ggplot2::theme(
title = ggplot2::element_text(size = 18, face = "bold"),
legend.title = ggplot2::element_text(size = 14, face = "bold"),
legend.text = ggplot2::element_text(size = 18, face = "bold"),
axis.title = ggplot2::element_text(size = 18, face = "bold"),
axis.text.y = ggplot2::element_text(size = 18, face = "bold"),
axis.text.x = ggplot2::element_text(size = 18, face = "bold"),
panel.background = ggplot2::element_blank(),
strip.text.x = ggplot2::element_text(
size = 18,
colour = "black",
face = "bold"
)
) +
ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(n = y.ticks)) +
ggplot2::scale_colour_manual(values = custom.myTAI.cols(nrow(mMatrix)))
if (!is.null(modules)) {
p <- p + ggplot2::geom_rect(data = mMatrix,ggplot2::aes(
xmin = modules[[2]][1],
xmax = modules[[2]][length(modules[[2]])],
ymin = min(MeanValsMatrix) - (min(MeanValsMatrix) / 50),
ymax = Inf), fill = "#4d004b", alpha = alpha)
}
p <- p + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 1,hjust = 1))
return(p)
}
if (length(Groups) == 2) {
mMatrixGroup1 <- dplyr::filter(mMatrix, age %in% Groups[[1]])
mMatrixGroup2 <- dplyr::filter(mMatrix, age %in% Groups[[2]])
p1 <- ggplot2::ggplot(mMatrixGroup1, ggplot2::aes( factor(stage, levels = unique(stage)), expr, group = age, fill = factor(age, levels = age_names[Groups[[1]]]))) +
ggplot2::geom_line(ggplot2::aes(color = factor(age, levels = age_names[Groups[[1]]])), size = 3) +
ggplot2::labs(x = xlab, y = ylab, title = main, colour = legendName) +
ggplot2::theme_minimal() +
ggplot2::theme(
title = ggplot2::element_text(size = 18, face = "bold"),
legend.title = ggplot2::element_text(size = 14, face = "bold"),
legend.text = ggplot2::element_text(size = 18, face = "bold"),
axis.title = ggplot2::element_text(size = 18, face = "bold"),
axis.text.y = ggplot2::element_text(size = 18, face = "bold"),
axis.text.x = ggplot2::element_text(size = 18, face = "bold"),
panel.background = ggplot2::element_blank(),
strip.text.x = ggplot2::element_text(
size = 18,
colour = "black",
face = "bold"
)
) +
ggplot2::scale_colour_manual(values = custom.myTAI.cols(nrow(mMatrix))[Groups[[1]]]) +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = -90, hjust = 0))
if (!adjust.range) {
p1 <- p1 + ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(n = y.ticks))
}
if (!is.null(modules)) {
p1 <- p1 + ggplot2::geom_rect(data = mMatrixGroup1, ggplot2::aes(
xmin = modules[[2]][1],
xmax = modules[[2]][length(modules[[2]])],
ymin = min(MeanValsMatrix),
ymax = Inf), fill = "#4d004b", alpha = alpha)
}
p2 <- ggplot2::ggplot(mMatrixGroup2, ggplot2::aes( factor(stage, levels = unique(stage)), expr, group = age, fill = factor(age, levels = age_names[Groups[[2]]]))) +
ggplot2::geom_line(ggplot2::aes(color = factor(age, levels = age_names[Groups[[2]]])), size = 3) +
ggplot2::labs(x = xlab, y = ylab, title = main, colour = legendName) +
ggplot2::theme_minimal() +
ggplot2::theme(
title = ggplot2::element_text(size = 18, face = "bold"),
legend.title = ggplot2::element_text(size = 14, face = "bold"),
legend.text = ggplot2::element_text(size = 18, face = "bold"),
axis.title = ggplot2::element_text(size = 18, face = "bold"),
axis.text.y = ggplot2::element_text(size = 18, face = "bold"),
axis.text.x = ggplot2::element_text(size = 18, face = "bold"),
panel.background = ggplot2::element_blank(),
strip.text.x = ggplot2::element_text(
size = 18,
colour = "black",
face = "bold"
)
) +
ggplot2::scale_colour_manual(values = custom.myTAI.cols(nrow(mMatrix))[Groups[[2]]]) +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = -90, hjust = 0))
if (!adjust.range) {
p2 <- p2 + ggplot2::scale_y_continuous(breaks = scales::pretty_breaks(n = y.ticks))
}
if (adjust.range){
p1 <- p1 + ggplot2::scale_y_continuous(limits = c(min(MeanValsMatrix), max(MeanValsMatrix)), breaks = scales::pretty_breaks(n = y.ticks))
p2 <- p2 + ggplot2::scale_y_continuous(limits = c(min(MeanValsMatrix), max(MeanValsMatrix)), breaks = scales::pretty_breaks(n = y.ticks))
}
if (!is.null(modules)) {
p2 <- p2 + ggplot2::geom_rect(data = mMatrixGroup2,ggplot2::aes(
xmin = modules[[2]][1],
xmax = modules[[2]][length(modules[[2]])],
ymin = min(MeanValsMatrix),
ymax = Inf), fill = "#4d004b", alpha = alpha)
}
p1 <- p1 + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 1,hjust = 1))
p2 <- p2 + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 1,hjust = 1))
return(gridExtra::grid.arrange(p1, p2, ncol = 2))
}
}
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