#' 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.
#'
#' @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}}{Violin plot of mean distribution.}
#' \item{\code{Variances}}{Violin plot 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$Plots)
#' @importFrom ggplot2 ggplot aes_string geom_point geom_smooth geom_boxplot
#' geom_violin scale_y_continuous scale_y_log10 scale_x_log10 xlab ylab ggtitle
#' theme_minimal
#' @importFrom scater cpm<-
#' @export
compareSCESets <- function(sces) {
checkmate::assertList(sces, types = "SCESet", any.missing = FALSE,
min.len = 1, names = "unique")
for (name in names(sces)) {
sce <- sces[[name]]
fData(sce)$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 = "mean_log_cpm",
colour = "Dataset")) +
geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) +
ylab(expression(paste("Mean ", log[2], "(CPM + 1)"))) +
ggtitle("Distribution of mean expression") +
theme_minimal()
vars <- ggplot(fData.all,
aes_string(x = "Dataset", y = "var_cpm",
colour = "Dataset")) +
geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) +
scale_y_log10(labels = scales::comma) +
ylab("CPM Variance") +
ggtitle("Distribution of variance") +
theme_minimal()
mean.var <- ggplot(fData.all,
aes_string(x = "mean_log_cpm", y = "var_log_cpm",
colour = "Dataset", fill = "Dataset")) +
geom_point(size = 0.1, alpha = 0.1) +
geom_smooth() +
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) +
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)) +
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)) +
ylab("Percentage zeros per cell") +
ggtitle("Distribution of zeros per cell") +
theme_minimal()
mean.zeros <- ggplot(fData.all,
aes_string(x = "mean_counts", y = "pct_dropout",
colour = "Dataset", fill = "Dataset")) +
geom_point(size = 0.1, alpha = 0.1) +
geom_smooth() +
scale_x_log10(labels = scales::comma) +
xlab("Mean count") +
ylab("Percentage zeros") +
ggtitle("Mean-dropout relationship") +
theme_minimal()
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
#'
#' @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
#' @importFrom scater cpm<-
#' @export
diffSCESets <- function(sces, ref) {
checkmate::assertList(sces, types = "SCESet", any.missing = FALSE,
min.len = 2, names = "unique")
checkmate::assertString(ref)
if (!(ref %in% names(sces))) {
stop("'ref' must be the name of an SCESet in '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)$mean_log_cpm)
ref.vars <- sort(fData(ref.sce)$var_log_cpm)
ref.libs <- sort(pData(ref.sce)$total_counts)
ref.z.gene <- sort(fData(ref.sce)$pct_dropout)
ref.z.cell <- sort(pData(ref.sce)$pct_dropout)
ref.vars.meanrank <- fData(ref.sce)$var_log_cpm[order(fData(ref.sce)$exprs_rank)]
ref.z.gene.meanrank <- fData(ref.sce)$pct_dropout[order(fData(ref.sce)$exprs_rank)]
for (name in names(sces)) {
sce <- sces[[name]]
fData(sce)$RefRankMeanLogCPM <- ref.means[rank(fData(sce)$mean_log_cpm)]
fData(sce)$RankDiffMeanLogCPM <- fData(sce)$mean_log_cpm -
fData(sce)$RefRankMeanLogCPM
fData(sce)$RefRankVarLogCPM <- ref.vars[rank(fData(sce)$var_log_cpm)]
fData(sce)$RankDiffVarLogCPM <- fData(sce)$var_log_cpm -
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)$MeanRankVarDiff <- fData(sce)$var_log_cpm -
ref.vars.meanrank[fData(sce)$exprs_rank]
fData(sce)$MeanRankZerosDiff <- fData(sce)$pct_dropout -
ref.z.gene.meanrank[fData(sce)$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() +
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() +
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")) +
geom_hline(yintercept = 0, colour = "red") +
geom_point() +
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() +
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() +
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() +
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")) +
geom_hline(yintercept = 0, colour = "red") +
geom_point() +
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 = "mean_log_cpm",
colour = "Dataset")) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
geom_point() +
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 = "var_log_cpm",
colour = "Dataset")) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
geom_point() +
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() +
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() +
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() +
xlab("Reference percentage zeros") +
ylab("Alternative percentage zeros") +
ggtitle("Ranked percentage zeros per cell") +
theme_minimal()
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)
}