#' Compare SCESet objects
#'
#' Combine the data from several SCESet objects and produce some basic plots
#' comparing them.
#'
#' @param sces named list of SCESet objects to combine and compare.
#' @param point.size size of points in scatter plots.
#' @param point.alpha opacity of points in scatter plots.
#' @param fits whether to include fits in scatter plots.
#' @param colours vector of colours to use for each dataset.
#'
#' @details
#' The returned list has three items:
#'
#' \describe{
#' \item{\code{FeatureData}}{Combined feature data from the provided
#' SCESets.}
#' \item{\code{PhenoData}}{Combined pheno data from the provided SCESets.}
#' \item{\code{Plots}}{Comparison plots
#' \describe{
#' \item{\code{Means}}{Boxplot of mean distribution.}
#' \item{\code{Variances}}{Boxplot of variance distribution.}
#' \item{\code{MeanVar}}{Scatter plot with fitted lines showing the
#' mean-variance relationship.}
#' \item{\code{LibraySizes}}{Boxplot of the library size
#' distribution.}
#' \item{\code{ZerosGene}}{Boxplot of the percentage of each gene
#' that is zero.}
#' \item{\code{ZerosCell}}{Boxplot of the percentage of each cell
#' that is zero.}
#' \item{\code{MeanZeros}}{Scatter plot with fitted lines showing
#' the mean-dropout relationship.}
#' }
#' }
#' }
#'
#' The plots returned by this function are created using
#' \code{\link[ggplot2]{ggplot}} and are only a sample of the kind of plots you
#' might like to consider. The data used to create these plots is also returned
#' and should be in the correct format to allow you to create further plots
#' using \code{\link[ggplot2]{ggplot}}.
#'
#' @return List containing the combined datasets and plots.
#' @examples
#' sim1 <- splatSimulate(nGenes = 1000, groupCells = 20)
#' sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
#' comparison <- compareSCESets(list(Splat = sim1, Simple = sim2))
#' names(comparison)
#' names(comparison$Plots)
#' @importFrom ggplot2 ggplot aes_string geom_point geom_smooth geom_boxplot
#' scale_y_continuous scale_y_log10 scale_x_log10 xlab ylab ggtitle
#' theme_minimal scale_colour_manual scale_fill_manual
#' @importFrom scater cpm<-
#' @export
compareSCESets <- function(sces, point.size = 0.1, point.alpha = 0.1,
fits = TRUE, colours = NULL) {
checkmate::assertList(sces, types = "SCESet", any.missing = FALSE,
min.len = 1, names = "unique")
checkmate::assertNumber(point.size, finite = TRUE)
checkmate::assertNumber(point.alpha, lower = 0, upper = 1)
checkmate::assertLogical(fits, any.missing = FALSE, len = 1)
if (!is.null(colours)) {
checkmate::assertCharacter(colours, any.missing = FALSE,
len = length(sces))
} else {
colours <- scales::hue_pal()(length(sces))
}
for (name in names(sces)) {
sce <- sces[[name]]
fData(sce)$Dataset <- name
pData(sce)$Dataset <- name
sce <- scater::calculateQCMetrics(sce)
cpm(sce) <- edgeR::cpm(counts(sce))
sce <- addFeatureStats(sce, "counts")
sce <- addFeatureStats(sce, "cpm")
sce <- addFeatureStats(sce, "cpm", log = TRUE)
sces[[name]] <- sce
}
fData.all <- fData(sces[[1]])
pData.all <- pData(sces[[1]])
if (length(sces) > 1) {
for (name in names(sces)[-1]) {
sce <- sces[[name]]
fData.all <- rbindMatched(fData.all, fData(sce))
pData.all <- rbindMatched(pData.all, pData(sce))
}
}
fData.all$Dataset <- factor(fData.all$Dataset, levels = names(sces))
pData.all$Dataset <- factor(pData.all$Dataset, levels = names(sces))
means <- ggplot(fData.all,
aes_string(x = "Dataset", y = "MeanLogCPM",
colour = "Dataset")) +
#geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) +
geom_boxplot() +
scale_colour_manual(values = colours) +
ylab(expression(paste("Mean ", log[2], "(CPM + 1)"))) +
ggtitle("Distribution of mean expression") +
theme_minimal()
vars <- ggplot(fData.all,
aes_string(x = "Dataset", y = "VarLogCPM",
colour = "Dataset")) +
#geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) +
geom_boxplot() +
scale_y_log10(labels = scales::comma) +
scale_colour_manual(values = colours) +
ylab(expression(paste("Variance ", log[2], "(CPM + 1)"))) +
ggtitle("Distribution of variance") +
theme_minimal()
mean.var <- ggplot(fData.all,
aes_string(x = "MeanLogCPM", y = "VarLogCPM",
colour = "Dataset", fill = "Dataset")) +
geom_point(size = point.size, alpha = point.alpha) +
scale_colour_manual(values = colours) +
scale_fill_manual(values = colours) +
xlab(expression(paste("Mean ", log[2], "(CPM + 1)"))) +
ylab(expression(paste("Variance ", log[2], "(CPM + 1)"))) +
ggtitle("Mean-variance relationship") +
theme_minimal()
libs <- ggplot(pData.all,
aes_string(x = "Dataset", y = "total_counts",
colour = "Dataset")) +
geom_boxplot() +
scale_y_continuous(labels = scales::comma) +
scale_colour_manual(values = colours) +
ylab("Total counts per cell") +
ggtitle("Distribution of library sizes") +
theme_minimal()
z.gene <- ggplot(fData.all,
aes_string(x = "Dataset", y = "pct_dropout",
colour = "Dataset")) +
geom_boxplot() +
scale_y_continuous(limits = c(0, 100)) +
scale_colour_manual(values = colours) +
ylab("Percentage zeros per gene") +
ggtitle("Distribution of zeros per gene") +
theme_minimal()
z.cell <- ggplot(pData.all,
aes_string(x = "Dataset", y = "pct_dropout",
colour = "Dataset")) +
geom_boxplot() +
scale_y_continuous(limits = c(0, 100)) +
scale_colour_manual(values = colours) +
ylab("Percentage zeros per cell") +
ggtitle("Distribution of zeros per cell") +
theme_minimal()
mean.zeros <- ggplot(fData.all,
aes_string(x = "MeanCounts", y = "pct_dropout",
colour = "Dataset", fill = "Dataset")) +
geom_point(size = point.size, alpha = point.alpha) +
scale_x_log10(labels = scales::comma) +
scale_colour_manual(values = colours) +
scale_fill_manual(values = colours) +
xlab("Mean count") +
ylab("Percentage zeros") +
ggtitle("Mean-dropout relationship") +
theme_minimal()
if (fits) {
mean.var <- mean.var + geom_smooth()
mean.zeros <- mean.zeros + geom_smooth()
}
comparison <- list(FeatureData = fData.all,
PhenoData = pData.all,
Plots = list(Means = means,
Variances = vars,
MeanVar = mean.var,
LibrarySizes = libs,
ZerosGene = z.gene,
ZerosCell = z.cell,
MeanZeros = mean.zeros))
return(comparison)
}
#' Diff SCESet objects
#'
#' Combine the data from several SCESet objects and produce some basic plots
#' comparing them to a reference.
#'
#' @param sces named list of SCESet objects to combine and compare.
#' @param ref string giving the name of the SCESet to use as the reference
#' @param point.size size of points in scatter plots.
#' @param point.alpha opacity of points in scatter plots.
#' @param fits whether to include fits in scatter plots.
#' @param colours vector of colours to use for each dataset.
#'
#' @details
#'
#' This function aims to look at the differences between a reference SCESet and
#' one or more others. It requires each SCESet to have the same dimensions.
#' Properties are compared by ranks, for example when comparing the means the
#' values are ordered and the differences between the reference and another
#' dataset plotted. A series of Q-Q plots are also returned.
#'
#' The returned list has five items:
#'
#' \describe{
#' \item{\code{Reference}}{The SCESet used as the reference.}
#' \item{\code{FeatureData}}{Combined feature data from the provided
#' SCESets.}
#' \item{\code{PhenoData}}{Combined pheno data from the provided SCESets.}
#' \item{\code{Plots}}{Difference plots
#' \describe{
#' \item{\code{Means}}{Boxplot of mean differences.}
#' \item{\code{Variances}}{Boxplot of variance differences.}
#' \item{\code{MeanVar}}{Scatter plot showing the difference from
#' the reference variance across expression ranks.}
#' \item{\code{LibraySizes}}{Boxplot of the library size
#' differences.}
#' \item{\code{ZerosGene}}{Boxplot of the differences in the
#' percentage of each gene that is zero.}
#' \item{\code{ZerosCell}}{Boxplot of the differences in the
#' percentage of each cell that is zero.}
#' \item{\code{MeanZeros}}{Scatter plot showing the difference from
#' the reference percentage of zeros across expression ranks.}
#' }
#' }
#' \item{\code{QQPlots}}{Quantile-Quantile plots
#' \describe{
#' \item{\code{Means}}{Q-Q plot of the means.}
#' \item{\code{Variances}}{Q-Q plot of the variances.}
#' \item{\code{LibrarySizes}}{Q-Q plot of the library sizes.}
#' \item{\code{ZerosGene}}{Q-Q plot of the percentage of zeros per
#' gene.}
#' \item{\code{ZerosCell}}{Q-Q plot of the percentage of zeros per
#' cell.}
#' }
#' }
#' }
#'
#' The plots returned by this function are created using
#' \code{\link[ggplot2]{ggplot}} and are only a sample of the kind of plots you
#' might like to consider. The data used to create these plots is also returned
#' and should be in the correct format to allow you to create further plots
#' using \code{\link[ggplot2]{ggplot}}.
#'
#' @return List containing the combined datasets and plots.
#' @examples
#' sim1 <- splatSimulate(nGenes = 1000, groupCells = 20)
#' sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
#' difference <- diffSCESets(list(Splat = sim1, Simple = sim2), ref = "Simple")
#' names(difference)
#' names(difference$Plots)
#' @importFrom ggplot2 ggplot aes_string geom_point geom_boxplot xlab ylab
#' ggtitle theme_minimal geom_hline geom_abline scale_colour_manual
#' scale_fill_manual
#' @importFrom scater cpm<-
#' @export
diffSCESets <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
fits = TRUE, colours = NULL) {
checkmate::assertList(sces, types = "SCESet", any.missing = FALSE,
min.len = 2, names = "unique")
checkmate::assertString(ref)
checkmate::assertNumber(point.size, finite = TRUE)
checkmate::assertNumber(point.alpha, lower = 0, upper = 1)
checkmate::assertLogical(fits, any.missing = FALSE, len = 1)
if (!(ref %in% names(sces))) {
stop("'ref' must be the name of an SCESet in 'sces'")
}
if (!is.null(colours)) {
checkmate::assertCharacter(colours, any.missing = FALSE,
len = length(sces) - 1)
} else {
colours <- scales::hue_pal()(length(sces))
}
ref.dim <- dim(sces[[ref]])
for (name in names(sces)) {
sce <- sces[[name]]
if (!identical(dim(sce), ref.dim)) {
stop("SCESets must have the same dimensions")
}
fData(sce)$Dataset <- name
pData(sce)$Dataset <- name
sce <- scater::calculateQCMetrics(sce)
cpm(sce) <- edgeR::cpm(counts(sce))
sce <- addFeatureStats(sce, "counts")
sce <- addFeatureStats(sce, "cpm", log = TRUE)
sces[[name]] <- sce
}
ref.sce <- sces[[ref]]
ref.means <- sort(fData(ref.sce)$MeanLogCPM)
ref.vars <- sort(fData(ref.sce)$VarLogCPM)
ref.libs <- sort(pData(ref.sce)$total_counts)
ref.z.gene <- sort(fData(ref.sce)$pct_dropout)
ref.z.cell <- sort(pData(ref.sce)$pct_dropout)
ref.rank.ord <- order(fData(ref.sce)$exprs_rank)
ref.vars.rank <- fData(ref.sce)$VarLogCPM[ref.rank.ord]
ref.z.gene.rank <- fData(ref.sce)$pct_dropout[ref.rank.ord]
for (name in names(sces)) {
sce <- sces[[name]]
fData(sce)$RefRankMeanLogCPM <- ref.means[rank(fData(sce)$MeanLogCPM)]
fData(sce)$RankDiffMeanLogCPM <- fData(sce)$MeanLogCPM -
fData(sce)$RefRankMeanLogCPM
fData(sce)$RefRankVarLogCPM <- ref.vars[rank(fData(sce)$VarLogCPM)]
fData(sce)$RankDiffVarLogCPM <- fData(sce)$VarLogCPM -
fData(sce)$RefRankVarLogCPM
pData(sce)$RefRankLibSize <- ref.libs[rank(pData(sce)$total_counts)]
pData(sce)$RankDiffLibSize <- pData(sce)$total_counts -
pData(sce)$RefRankLibSize
fData(sce)$RefRankZeros <- ref.z.gene[rank(fData(sce)$pct_dropout)]
fData(sce)$RankDiffZeros <- fData(sce)$pct_dropout -
fData(sce)$RefRankZeros
pData(sce)$RefRankZeros <- ref.z.cell[rank(pData(sce)$pct_dropout)]
pData(sce)$RankDiffZeros <- pData(sce)$pct_dropout -
pData(sce)$RefRankZeros
fData(sce)$MeanRankVarDiff <- fData(sce)$VarLogCPM -
ref.vars.rank[fData(sce)$exprs_rank]
fData(sce)$MeanRankZerosDiff <- fData(sce)$pct_dropout -
ref.z.gene.rank[fData(sce)$exprs_rank]
sces[[name]] <- sce
}
ref.sce <- sces[[ref]]
sces[[ref]] <- NULL
fData.all <- fData(sces[[1]])
pData.all <- pData(sces[[1]])
if (length(sces) > 1) {
for (name in names(sces)[-1]) {
sce <- sces[[name]]
fData.all <- rbindMatched(fData.all, fData(sce))
pData.all <- rbindMatched(pData.all, pData(sce))
}
}
fData.all$Dataset <- factor(fData.all$Dataset, levels = names(sces))
pData.all$Dataset <- factor(pData.all$Dataset, levels = names(sces))
means <- ggplot(fData.all,
aes_string(x = "Dataset", y = "RankDiffMeanLogCPM",
colour = "Dataset")) +
geom_hline(yintercept = 0, colour = "red") +
geom_boxplot() +
scale_colour_manual(values = colours) +
ylab(expression(paste("Rank difference mean ", log[2], "(CPM + 1)"))) +
ggtitle("Difference in mean expression") +
theme_minimal()
vars <- ggplot(fData.all,
aes_string(x = "Dataset", y = "RankDiffVarLogCPM",
colour = "Dataset")) +
geom_hline(yintercept = 0, colour = "red") +
geom_boxplot() +
scale_colour_manual(values = colours) +
ylab(expression(paste("Rank difference variance ", log[2],
"(CPM + 1)"))) +
ggtitle("Difference in variance") +
theme_minimal()
mean.var <- ggplot(fData.all,
aes_string(x = "exprs_rank", y = "MeanRankVarDiff",
colour = "Dataset", fill = "Dataset")) +
geom_hline(yintercept = 0, colour = "red") +
geom_point(size = point.size, alpha = point.alpha) +
scale_colour_manual(values = colours) +
scale_fill_manual(values = colours) +
xlab("Expression rank") +
ylab(expression(paste("Difference in variance ", log[2],
"(CPM + 1)"))) +
ggtitle("Difference in mean-variance relationship") +
theme_minimal()
libs <- ggplot(pData.all,
aes_string(x = "Dataset", y = "RankDiffLibSize",
colour = "Dataset")) +
geom_hline(yintercept = 0, colour = "red") +
geom_boxplot() +
scale_colour_manual(values = colours) +
ylab(paste("Rank difference libray size")) +
ggtitle("Difference in library sizes") +
theme_minimal()
z.gene <- ggplot(fData.all,
aes_string(x = "Dataset", y = "RankDiffZeros",
colour = "Dataset")) +
geom_hline(yintercept = 0, colour = "red") +
geom_boxplot() +
scale_colour_manual(values = colours) +
ylab(paste("Rank difference percentage zeros")) +
ggtitle("Difference in zeros per gene") +
theme_minimal()
z.cell <- ggplot(pData.all,
aes_string(x = "Dataset", y = "RankDiffZeros",
colour = "Dataset")) +
geom_hline(yintercept = 0, colour = "red") +
geom_boxplot() +
scale_colour_manual(values = colours) +
ylab(paste("Rank difference percentage zeros")) +
ggtitle("Difference in zeros per cell") +
theme_minimal()
mean.zeros <- ggplot(fData.all,
aes_string(x = "exprs_rank", y = "MeanRankZerosDiff",
colour = "Dataset", fill = "Dataset")) +
geom_hline(yintercept = 0, colour = "red") +
geom_point(size = point.size, alpha = point.alpha) +
scale_colour_manual(values = colours) +
scale_fill_manual(values = colours) +
xlab("Expression rank") +
ylab("Difference in percentage zeros per gene") +
ggtitle("Difference in mean-zeros relationship") +
theme_minimal()
means.qq <- ggplot(fData.all,
aes_string(x = "RefRankMeanLogCPM", y = "MeanLogCPM",
colour = "Dataset")) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
geom_point(size = point.size, alpha = point.alpha) +
scale_colour_manual(values = colours) +
xlab(expression(paste("Reference mean ", log[2], "(CPM + 1)"))) +
ylab(expression(paste("Alternative mean ", log[2], "(CPM + 1)"))) +
ggtitle("Ranked means") +
theme_minimal()
vars.qq <- ggplot(fData.all,
aes_string(x = "RefRankVarLogCPM", y = "VarLogCPM",
colour = "Dataset")) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
geom_point(size = point.size, alpha = point.alpha) +
scale_colour_manual(values = colours) +
xlab(expression(paste("Reference variance ", log[2], "(CPM + 1)"))) +
ylab(expression(paste("Alternative variance ", log[2], "(CPM + 1)"))) +
ggtitle("Ranked variances") +
theme_minimal()
libs.qq <- ggplot(pData.all,
aes_string(x = "RefRankLibSize", y = "total_counts",
colour = "Dataset")) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
geom_point(size = point.size, alpha = point.alpha) +
scale_colour_manual(values = colours) +
xlab("Reference library size") +
ylab("Alternative library size") +
ggtitle("Ranked library sizes") +
theme_minimal()
z.gene.qq <- ggplot(fData.all,
aes_string(x = "RefRankZeros", y = "pct_dropout",
colour = "Dataset")) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
geom_point(size = point.size, alpha = point.alpha) +
scale_colour_manual(values = colours) +
xlab("Reference percentage zeros") +
ylab("Alternative percentage zeros") +
ggtitle("Ranked percentage zeros per gene") +
theme_minimal()
z.cell.qq <- ggplot(pData.all,
aes_string(x = "RefRankZeros", y = "pct_dropout",
colour = "Dataset")) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
geom_point(size = point.size, alpha = point.alpha) +
scale_colour_manual(values = colours) +
xlab("Reference percentage zeros") +
ylab("Alternative percentage zeros") +
ggtitle("Ranked percentage zeros per cell") +
theme_minimal()
if (fits) {
mean.var <- mean.var + geom_smooth()
mean.zeros <- mean.zeros + geom_smooth()
}
comparison <- list(Reference = ref.sce,
FeatureData = fData.all,
PhenoData = pData.all,
Plots = list(Means = means,
Variances = vars,
MeanVar = mean.var,
LibrarySizes = libs,
ZerosGene = z.gene,
ZerosCell = z.cell,
MeanZeros = mean.zeros),
QQPlots = list(Means = means.qq,
Variances = vars.qq,
LibrarySizes = libs.qq,
ZerosGene = z.gene.qq,
ZerosCell = z.cell.qq))
return(comparison)
}
#' Make comparison panel
#'
#' Combine the plots from \code{compareSCESets} into a single panel.
#'
#' @param comp list returned by \code{\link{compareSCESets}}.
#' @param title title for the panel.
#' @param labels vector of labels for each of the seven plots.
#'
#' @return Combined panel plot
#'
#' @examples
#' \dontrun{
#' sim1 <- splatSimulate(nGenes = 1000, groupCells = 20)
#' sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
#' comparison <- compareSCESets(list(Splat = sim1, Simple = sim2))
#' panel <- makeCompPanel(comparison)
#' }
#'
#' @importFrom ggplot2 theme element_blank
#' @export
makeCompPanel <- function(comp, title = "Comparison",
labels = c("Means", "Variance",
"Mean-variance relationship",
"Library size", "Zeros per gene",
"Zeros per cell",
"Mean-zeros relationship")) {
if (!requireNamespace("cowplot", quietly = TRUE)) {
stop("The `cowplot` package is required to make panels.")
}
checkmate::assertList(comp, any.missing = FALSE, len = 3)
checkmate::checkString(title)
checkmate::checkCharacter(labels, len = 7)
plots <- list(p1 = comp$Plots$Means, p2 = comp$Plots$Variances,
p3 = comp$Plots$MeanVar, p4 = comp$Plots$LibrarySizes,
p5 = comp$Plots$ZerosGene, p6 = comp$Plots$ZerosCell,
p7 = comp$Plots$MeanZeros)
# Remove titles and legends
for (plot in names(plots)) {
plots[[plot]] <- plots[[plot]] + theme(legend.position = "none",
plot.title = element_blank())
}
# Remove x-axis title from some plots
for (plot in paste0("p", c(1, 2, 4, 5, 6))) {
plots[[plot]] <- plots[[plot]] + theme(axis.title.x = element_blank())
}
plots$leg <- cowplot::get_legend(plots$p1 +
theme(legend.position = "bottom"))
panel <- cowplot::ggdraw() +
cowplot::draw_label(title, 0.5, 0.98,
fontface = "bold", size = 18) +
cowplot::draw_label(labels[1], 0.01, 0.95,
fontface = "bold", hjust = 0, vjust = 0) +
cowplot::draw_plot(plots$p1, 0.0, 0.74, 0.5, 0.20) +
cowplot::draw_label(labels[2], 0.51, 0.95,
fontface = "bold", hjust = 0, vjust = 0) +
cowplot::draw_plot(plots$p2, 0.5, 0.74, 0.5, 0.20) +
cowplot::draw_label(labels[3], 0.01, 0.70,
fontface = "bold", hjust = 0, vjust = 0) +
cowplot::draw_plot(plots$p3, 0.0, 0.49, 0.5, 0.20) +
cowplot::draw_label(labels[4], 0.51, 0.70,
fontface = "bold", hjust = 0, vjust = 0) +
cowplot::draw_plot(plots$p4, 0.5, 0.49, 0.5, 0.20) +
cowplot::draw_label(labels[5], 0.01, 0.45,
fontface = "bold", hjust = 0, vjust = 0) +
cowplot::draw_plot(plots$p5, 0.0, 0.24, 0.5, 0.20) +
cowplot::draw_label(labels[6], 0.51, 0.45,
fontface = "bold", hjust = 0, vjust = 0) +
cowplot::draw_plot(plots$p6, 0.5, 0.24, 0.5, 0.20) +
cowplot::draw_label(labels[7], 0.01, 0.21,
fontface = "bold", hjust = 0, vjust = 0) +
cowplot::draw_plot(plots$p7, 0.0, 0.00, 0.5, 0.20) +
cowplot::draw_plot(plots$leg, 0.5, 0.00, 0.5, 0.20)
return(panel)
}
#' Make difference panel
#'
#' Combine the plots from \code{diffSCESets} into a single panel.
#'
#' @param diff list returned by \code{\link{diffSCESets}}.
#' @param title title for the panel.
#' @param labels vector of labels for each of the seven sections.
#'
#' @return Combined panel plot
#'
#' @examples
#' \dontrun{
#' sim1 <- splatSimulate(nGenes = 1000, groupCells = 20)
#' sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
#' difference <- diffSCESets(list(Splat = sim1, Simple = sim2), ref = "Simple")
#' panel <- makeDiffPanel(difference)
#' }
#'
#' @importFrom ggplot2 theme element_blank
#' @export
makeDiffPanel <- function(diff, title = "Difference comparison",
labels = c("Means", "Variance", "Library size",
"Zeros per cell", "Zeros per gene",
"Mean-variance relationship",
"Mean-zeros relationship")) {
if (!requireNamespace("cowplot", quietly = TRUE)) {
stop("The `cowplot` package is required to make panels.")
}
checkmate::assertList(diff, any.missing = FALSE, len = 5)
checkmate::checkString(title)
checkmate::checkCharacter(labels, len = 7)
plots <- list(p1 = diff$Plots$Means, p2 = diff$QQPlots$Means,
p3 = diff$Plots$Variances, p4 = diff$QQPlots$Variances,
p5 = diff$Plots$MeanVar, p6 = diff$Plots$LibrarySizes,
p7 = diff$QQPlots$LibrarySizes, p8 = diff$Plots$ZerosCell,
p9 = diff$QQPlots$ZerosCell, p10 = diff$Plots$ZerosGene,
p11 = diff$QQPlots$ZerosGene, p12 = diff$Plots$MeanZeros)
# Remove titles and legends
for (plot in names(plots)) {
plots[[plot]] <- plots[[plot]] + theme(legend.position = "none",
plot.title = element_blank())
}
# Remove x-axis title from some plots
for (plot in paste0("p", c(1, 3, 6, 8, 10))) {
plots[[plot]] <- plots[[plot]] + theme(axis.title.x = element_blank())
}
plots$leg <- cowplot::get_legend(plots$p1 +
theme(legend.position = "bottom"))
panel <- cowplot::ggdraw() +
cowplot::draw_label(title, 0.5, 0.98,
fontface = "bold", size = 18) +
cowplot::draw_label(labels[1], 0.0, 0.94,
fontface = "bold", hjust = 0, vjust = 0) +
cowplot::draw_plot(plots$p1, 0.0, 0.64, 0.18, 0.29) +
cowplot::draw_plot(plots$p2, 0.0, 0.32, 0.18, 0.29) +
cowplot::draw_label(labels[2], 0.21, 0.94,
fontface = "bold", hjust = 0, vjust = 0) +
cowplot::draw_plot(plots$p3, 0.21, 0.64, 0.18, 0.29) +
cowplot::draw_plot(plots$p4, 0.21, 0.32, 0.18, 0.29) +
cowplot::draw_label(labels[6], 0.0, 0.30,
fontface = "bold", hjust = 0, vjust = 0) +
cowplot::draw_plot(plots$p5, 0.0, 0.0, 0.38, 0.29) +
cowplot::draw_label(labels[3], 0.41, 0.94,
fontface = "bold", hjust = 0, vjust = 0) +
cowplot::draw_plot(plots$p6, 0.41, 0.64, 0.18, 0.29) +
cowplot::draw_plot(plots$p7, 0.41, 0.32, 0.18, 0.29) +
cowplot::draw_label(labels[4], 0.61, 0.94,
fontface = "bold", hjust = 0, vjust = 0) +
cowplot::draw_plot(plots$p8, 0.61, 0.64, 0.18, 0.29) +
cowplot::draw_plot(plots$p9, 0.61, 0.32, 0.18, 0.29) +
cowplot::draw_label(labels[7], 0.41, 0.30,
fontface = "bold", hjust = 0, vjust = 0) +
cowplot::draw_plot(plots$p12, 0.41, 0.0, 0.38, 0.29) +
cowplot::draw_label(labels[5], 0.81, 0.94,
fontface = "bold", hjust = 0, vjust = 0) +
cowplot::draw_plot(plots$p10, 0.81, 0.64, 0.18, 0.29) +
cowplot::draw_plot(plots$p11, 0.81, 0.32, 0.18, 0.29) +
cowplot::draw_plot(plots$leg, 0.81, 0.0, 0.2, 0.29)
return(panel)
}
#' Make overall panel
#'
#' Combine the plots from \code{compSCESets} and \code{diffSCESets} into a
#' single panel.
#'
#' @param comp list returned by \code{\link{compareSCESets}}.
#' @param diff list returned by \code{\link{diffSCESets}}.
#' @param title title for the panel.
#' @param row.labels vector of labels for each of the seven rows.
#'
#' @return Combined panel plot
#'
#' @examples
#' \dontrun{
#' sim1 <- splatSimulate(nGenes = 1000, groupCells = 20)
#' sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
#' comparison <- compSCESets(list(Splat = sim1, Simple = sim2))
#' difference <- diffSCESets(list(Splat = sim1, Simple = sim2), ref = "Simple")
#' panel <- makeOverallPanel(comparison, difference)
#' }
#'
#' @importFrom ggplot2 theme element_blank
#' @export
makeOverallPanel <- function(comp, diff, title = "Overall comparison",
row.labels = c("Means", "Variance",
"Mean-variance relationship",
"Library size", "Zeros per cell",
"Zeros per gene",
"Mean-zeros relationship")) {
if (!requireNamespace("cowplot", quietly = TRUE)) {
stop("The `cowplot` package is required to make panels.")
}
checkmate::assertList(comp, any.missing = FALSE, len = 3)
checkmate::assertList(diff, any.missing = FALSE, len = 5)
checkmate::checkString(title)
checkmate::checkCharacter(row.labels, len = 7)
plots <- list(p1 = comp$Plots$Means, p2 = diff$Plots$Means,
p3 = diff$QQPlots$Means, p4 = comp$Plots$Variances,
p5 = diff$Plots$Variances, p6 = diff$QQPlots$Variances,
p7 = comp$Plots$MeanVar, p8 = diff$Plots$MeanVar,
p9 = comp$Plots$LibrarySizes, p10 = diff$Plots$LibrarySizes,
p11 = diff$QQPlots$LibrarySizes, p12 = comp$Plots$ZerosCell,
p13 = diff$Plots$ZerosCell, p14 = diff$QQPlots$ZerosCell,
p15 = comp$Plots$ZerosGene, p16 = diff$Plots$ZerosGene,
p17 = diff$QQPlots$ZerosGene, p18 = comp$Plots$MeanZeros,
p19 = diff$Plots$MeanZeros)
# Remove titles and legends
for (plot in names(plots)) {
plots[[plot]] <- plots[[plot]] + theme(legend.position = "none",
plot.title = element_blank())
}
# Remove x-axis title from some plots
for (plot in paste0("p", c(1, 2, 4, 5, 9, 10, 12, 13, 15, 16))) {
plots[[plot]] <- plots[[plot]] + theme(axis.title.x = element_blank())
}
plots$leg <- cowplot::get_legend(plots$p1 +
theme(legend.position = "bottom"))
panel <- cowplot::ggdraw() +
cowplot::draw_label(title, 0.5, 0.995,
fontface = "bold", size = 18) +
cowplot::draw_label(row.labels[1], 0.01, 0.985,
fontface = "bold", hjust = 0, vjust = 0) +
cowplot::draw_plot(plots$p1, 0.00, 0.86, 0.32, 0.12) +
cowplot::draw_plot(plots$p2, 0.34, 0.86, 0.32, 0.12) +
cowplot::draw_plot(plots$p3, 0.67, 0.86, 0.32, 0.12) +
cowplot::draw_label(row.labels[2], 0.01, 0.845,
fontface = "bold", hjust = 0, vjust = 0) +
cowplot::draw_plot(plots$p4, 0.00, 0.72, 0.32, 0.12) +
cowplot::draw_plot(plots$p5, 0.34, 0.72, 0.32, 0.12) +
cowplot::draw_plot(plots$p6, 0.67, 0.72, 0.32, 0.12) +
cowplot::draw_label(row.labels[3], 0.01, 0.705,
fontface = "bold", hjust = 0, vjust = 0) +
cowplot::draw_plot(plots$p7, 0.00, 0.58, 0.49, 0.12) +
cowplot::draw_plot(plots$p8, 0.51, 0.58, 0.49, 0.12) +
cowplot::draw_label(row.labels[4], 0.01, 0.56,
fontface = "bold", hjust = 0, vjust = 0) +
cowplot::draw_plot(plots$p9, 0.00, 0.44, 0.32, 0.12) +
cowplot::draw_plot(plots$p10, 0.34, 0.44, 0.32, 0.12) +
cowplot::draw_plot(plots$p11, 0.67, 0.44, 0.32, 0.12) +
cowplot::draw_label(row.labels[5], 0.01, 0.425,
fontface = "bold", hjust = 0, vjust = 0) +
cowplot::draw_plot(plots$p12, 0.00, 0.30, 0.32, 0.12) +
cowplot::draw_plot(plots$p13, 0.34, 0.30, 0.32, 0.12) +
cowplot::draw_plot(plots$p14, 0.67, 0.30, 0.32, 0.12) +
cowplot::draw_label(row.labels[6], 0.01, 0.285,
fontface = "bold", hjust = 0, vjust = 0) +
cowplot::draw_plot(plots$p15, 0.00, 0.16, 0.32, 0.12) +
cowplot::draw_plot(plots$p16, 0.34, 0.16, 0.32, 0.12) +
cowplot::draw_plot(plots$p17, 0.67, 0.16, 0.32, 0.12) +
cowplot::draw_label(row.labels[7], 0.01, 0.145,
fontface = "bold", hjust = 0, vjust = 0) +
cowplot::draw_plot(plots$p18, 0.00, 0.02, 0.49, 0.12) +
cowplot::draw_plot(plots$p19, 0.51, 0.02, 0.49, 0.12) +
cowplot::draw_plot(plots$leg, 0.00, 0.00, 1.00, 0.02)
return(panel)
}