diff --git a/DESCRIPTION b/DESCRIPTION
index e6aee3ec4b01f7419dce8ab71d6ec8afb296607d..5b99b77dab69425790e5ae6b839266d44d0a6f68 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,8 +1,8 @@
Package: splatter
Type: Package
Title: Simple Simulation of Single-cell RNA Sequencing Data
-Version: 0.99.11
-Date: 2017-03-20
+Version: 0.99.12
+Date: 2017-03-22
Author: Luke Zappia
Authors@R:
c(person("Luke", "Zappia", role = c("aut", "cre"),
@@ -51,5 +51,5 @@ biocViews: SingleCell, RNASeq, Transcriptomics, GeneExpression, Sequencing,
Software
URL: https://github.com/Oshlack/splatter
BugReports: https://github.com/Oshlack/splatter/issues
-RoxygenNote: 6.0.0
+RoxygenNote: 6.0.1
VignetteBuilder: knitr
diff --git a/NAMESPACE b/NAMESPACE
index 4e1ed1b1e5156d9606895f2d4f1f2e5951f0cbc1..c00aebb4c71329718aad6b095bda5f76c2abe6de 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -12,6 +12,7 @@ S3method(splatEstimate,SCESet)
S3method(splatEstimate,matrix)
export(addGeneLengths)
export(compareSCESets)
+export(diffSCESets)
export(getParam)
export(getParams)
export(listSims)
@@ -57,7 +58,9 @@ importFrom(checkmate,checkLogical)
importFrom(checkmate,checkNumber)
importFrom(checkmate,checkNumeric)
importFrom(ggplot2,aes_string)
+importFrom(ggplot2,geom_abline)
importFrom(ggplot2,geom_boxplot)
+importFrom(ggplot2,geom_hline)
importFrom(ggplot2,geom_point)
importFrom(ggplot2,geom_smooth)
importFrom(ggplot2,geom_violin)
diff --git a/NEWS.md b/NEWS.md
index 4877c31f0e88ed359e670b1d1b5447ad310b8678..308481dee215ae38435b069d9ff3024d2b365467 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,11 @@
+# 0.99.12
+
+* Add diffSCESets function
+* Update compareSCESets plots
+* Modify Lun2 nGenes estimate
+* Modify how addFeatureStats names columns
+* Add infinte bcv.df warning to splatSimulate
+
# 0.99.11
* Add parallel option to lun2Estimate
diff --git a/R/SCESet-functions.R b/R/SCESet-functions.R
index 63178257a4053fd8b9580561372e3f4b26048d88..2a92c3545c2aa80652533158bbd89168b65f5735 100644
--- a/R/SCESet-functions.R
+++ b/R/SCESet-functions.R
@@ -14,8 +14,10 @@
#' @details
#' Currently adds the following statistics: mean, variance, coefficient of
#' variation, median and median absolute deviation. Statistics are added to
-#' the \code{fData} slot and are named \code{stat_[log]_value_[no0]} where
-#' \code{log} and \code{no0} are added if those arguments are true.
+#' the \code{fData} slot and are named \code{Stat[Log]Value[No0]} where
+#' \code{Log} and \code{No0} are added if those arguments are true.
+#' UpperCamelCase is used to differentiate these columns from those added by
+#' \code{scater}.
#'
#' @return SCESet with additional feature statistics
#'
@@ -28,35 +30,37 @@ addFeatureStats <- function(sce, value = c("counts", "cpm", "tpm", "fpkm"),
switch(value,
counts = {
values = scater::counts(sce)
+ suffix <- "Counts"
},
cpm = {
values = scater::cpm(sce)
+ suffix <- "CPM"
},
tpm = {
values = scater::tpm(sce)
+ suffix <- "TPM"
},
fpkm = {
values = scater::fpkm(sce)
+ suffix <- "FPKM"
}
)
- suffix <- value
-
if (no.zeros) {
values[values == 0] <- NA
- suffix = paste0(suffix, "_no0")
+ suffix = paste0(suffix, "No0")
}
if (log) {
values = log2(values + offset)
- suffix = paste0("log_", suffix)
+ suffix = paste0("Log", suffix)
}
- mean.str <- paste0("mean_", suffix)
- var.str <- paste0("var_", suffix)
- cv.str <- paste0("cv_", suffix)
- med.str <- paste0("med_", suffix)
- mad.str <- paste0("mad_", suffix)
+ mean.str <- paste0("Mean", suffix)
+ var.str <- paste0("Var", suffix)
+ cv.str <- paste0("CV", suffix)
+ med.str <- paste0("Med", suffix)
+ mad.str <- paste0("MAD", suffix)
fData(sce)[, mean.str] <- rowMeans(values, na.rm = TRUE)
fData(sce)[, var.str] <- matrixStats::rowVars(values, na.rm = TRUE)
diff --git a/R/compare.R b/R/compare.R
index 76de2ab8eaf18d0a0ebadd2aa3435c64fc40594e..23aa2710ccaf96391156870e6d71c745eb749580 100644
--- a/R/compare.R
+++ b/R/compare.R
@@ -1,12 +1,12 @@
#' Compare SCESet objects
#'
#' Combine the data from several SCESet objects and produce some basic plots
-#' comparing.
+#' comparing them.
#'
#' @param sces named list of SCESet objects to combine and compare.
#'
#' @details
-#' The return list has three items:
+#' The returned list has three items:
#'
#' \describe{
#' \item{\code{FeatureData}}{Combined feature data from the provided
@@ -14,8 +14,8 @@
#' \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{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
@@ -80,24 +80,26 @@ compareSCESets <- function(sces) {
pData.all$Dataset <- factor(pData.all$Dataset, levels = names(sces))
means <- ggplot(fData.all,
- aes_string(x = "Dataset", y = "mean_log_cpm",
+ aes_string(x = "Dataset", y = "MeanLogCPM",
colour = "Dataset")) +
- geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) +
+ #geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) +
+ geom_boxplot() +
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",
+ aes_string(x = "Dataset", y = "VarCPM",
colour = "Dataset")) +
- geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) +
+ #geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) +
+ geom_boxplot() +
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",
+ aes_string(x = "MeanLogCPM", y = "VarLogCPM",
colour = "Dataset", fill = "Dataset")) +
geom_point(size = 0.1, alpha = 0.1) +
geom_smooth() +
@@ -134,7 +136,7 @@ compareSCESets <- function(sces) {
theme_minimal()
mean.zeros <- ggplot(fData.all,
- aes_string(x = "mean_counts", y = "pct_dropout",
+ aes_string(x = "MeanCounts", y = "pct_dropout",
colour = "Dataset", fill = "Dataset")) +
geom_point(size = 0.1, alpha = 0.1) +
geom_smooth() +
@@ -156,3 +158,289 @@ compareSCESets <- function(sces) {
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$Plots)
+#' @importFrom ggplot2 ggplot aes_string geom_point geom_boxplot xlab ylab
+#' ggtitle theme_minimal geom_hline geom_abline
+#' @importFrom scater cpm<-
+#' @export
+diffSCESets <- function(sces, ref) {
+
+ checkmate::assertList(sces, types = "SCESet", any.missing = FALSE,
+ min.len = 2, names = "unique")
+ checkmate::assertString(ref)
+
+ if (!(ref %in% names(sces))) {
+ stop("'ref' must be the name of an SCESet in 'sces'")
+ }
+
+ ref.dim <- dim(sces[[ref]])
+
+ for (name in names(sces)) {
+ sce <- sces[[name]]
+ if (!identical(dim(sce), ref.dim)) {
+ stop("SCESets must have the same dimensions")
+ }
+ fData(sce)$Dataset <- name
+ pData(sce)$Dataset <- name
+ sce <- scater::calculateQCMetrics(sce)
+ cpm(sce) <- edgeR::cpm(counts(sce))
+ sce <- addFeatureStats(sce, "counts")
+ sce <- addFeatureStats(sce, "cpm", log = TRUE)
+ sces[[name]] <- sce
+ }
+
+ ref.sce <- sces[[ref]]
+
+ ref.means <- sort(fData(ref.sce)$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() +
+ 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 = "MeanLogCPM",
+ 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 = "VarLogCPM",
+ 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)
+}
diff --git a/R/lun2-estimate.R b/R/lun2-estimate.R
index cde774baf9b402bb2baa54e07a38caaa35718e82..6a5f53009fe549522faabb49f478b03430fc0849 100644
--- a/R/lun2-estimate.R
+++ b/R/lun2-estimate.R
@@ -185,7 +185,7 @@ lun2Estimate.matrix <- function(counts, plates, params = newLun2Params(),
zinb.prop <- exp(zinb.prop) / (1 + exp(zinb.prop))
- params <- setParams(params, nGenes = length(logmeans),
+ params <- setParams(params, nGenes = nrow(counts),
cell.plates = plates, plate.var = sigma2,
gene.means = exp(logmeans),
gene.disps = dge$tagwise.dispersion,
diff --git a/R/splat-simulate.R b/R/splat-simulate.R
index 70a987d72786c51cfc31f321805de3da84bb5e76..1feb4cf6d31423aa940820fee2dfe9bb39059b2c 100644
--- a/R/splat-simulate.R
+++ b/R/splat-simulate.R
@@ -192,7 +192,7 @@ splatSimulate <- function(params = newSplatParams(),
sim <- splatSimBCVMeans(sim, params)
if (verbose) {message("Simulating counts..")}
sim <- splatSimTrueCounts(sim, params)
- if (verbose) {message("Simulating dropout...")}
+ if (verbose) {message("Simulating dropout (if needed)...")}
sim <- splatSimDropout(sim, params)
if (verbose) {message("Creating final SCESet...")}
@@ -537,8 +537,13 @@ splatSimBCVMeans <- function(sim, params) {
bcv.df <- getParam(params, "bcv.df")
base.means.cell <- get_exprs(sim, "BaseCellMeans")
- bcv <- (bcv.common + (1 / sqrt(base.means.cell))) *
- sqrt(bcv.df / rchisq(nGenes, df = bcv.df))
+ if (is.finite(bcv.df)) {
+ bcv <- (bcv.common + (1 / sqrt(base.means.cell))) *
+ sqrt(bcv.df / rchisq(nGenes, df = bcv.df))
+ } else {
+ warning("'bcv.df' is infinite. This parameter will be ignored.")
+ bcv <- (bcv.common + (1 / sqrt(base.means.cell)))
+ }
means.cell <- matrix(rgamma(nGenes * nCells, shape = 1 / (bcv ^ 2),
scale = base.means.cell * (bcv ^ 2)),
diff --git a/man/addFeatureStats.Rd b/man/addFeatureStats.Rd
index 8d454be2663c89d8ccada477845e84b212f36550..f4f72ef4cd05c914a0cac48746a6118102d5f1ed 100644
--- a/man/addFeatureStats.Rd
+++ b/man/addFeatureStats.Rd
@@ -30,6 +30,8 @@ Add additional feature statistics to an SCESet object
\details{
Currently adds the following statistics: mean, variance, coefficient of
variation, median and median absolute deviation. Statistics are added to
-the \code{fData} slot and are named \code{stat_[log]_value_[no0]} where
-\code{log} and \code{no0} are added if those arguments are true.
+the \code{fData} slot and are named \code{Stat[Log]Value[No0]} where
+\code{Log} and \code{No0} are added if those arguments are true.
+UpperCamelCase is used to differentiate these columns from those added by
+\code{scater}.
}
diff --git a/man/compareSCESets.Rd b/man/compareSCESets.Rd
index 14eb59dca842ebf7e10f78a612da9155b6b73a8c..e0533d3114487674ac807388762d2e1f1c41cb28 100644
--- a/man/compareSCESets.Rd
+++ b/man/compareSCESets.Rd
@@ -14,10 +14,10 @@ List containing the combined datasets and plots.
}
\description{
Combine the data from several SCESet objects and produce some basic plots
-comparing.
+comparing them.
}
\details{
-The return list has three items:
+The returned list has three items:
\describe{
\item{\code{FeatureData}}{Combined feature data from the provided
@@ -25,8 +25,8 @@ The return list has three items:
\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{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
diff --git a/man/diffSCESets.Rd b/man/diffSCESets.Rd
new file mode 100644
index 0000000000000000000000000000000000000000..3fc3e60bcb04fb6ca0ed1de07fe9d8439fc48e20
--- /dev/null
+++ b/man/diffSCESets.Rd
@@ -0,0 +1,76 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/compare.R
+\name{diffSCESets}
+\alias{diffSCESets}
+\title{Diff SCESet objects}
+\usage{
+diffSCESets(sces, ref)
+}
+\arguments{
+\item{sces}{named list of SCESet objects to combine and compare.}
+
+\item{ref}{string giving the name of the SCESet to use as the reference}
+}
+\value{
+List containing the combined datasets and plots.
+}
+\description{
+Combine the data from several SCESet objects and produce some basic plots
+comparing them to a 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}}.
+}
+\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)
+}
diff --git a/tests/testthat/test-splat-simulate.R b/tests/testthat/test-splat-simulate.R
index d5e329a4f2da69dce7749e2611acf0e31acf697e..ad563b74670749f1ae0f5f8642ac2b6fc2a4119a 100644
--- a/tests/testthat/test-splat-simulate.R
+++ b/tests/testthat/test-splat-simulate.R
@@ -14,4 +14,9 @@ test_that("one group switches to single mode", {
"nGroups is 1, switching to single mode")
expect_silent(splatSimulate(test.params, method = "paths",
groupCells = c(10), verbose = FALSE))
-})
\ No newline at end of file
+})
+
+test_that("infinite bcv.df is detected", {
+ expect_warning(splatSimulate(test.params, bcv.df = Inf),
+ "'bcv.df' is infinite. This parameter will be ignored.")
+})
diff --git a/vignettes/splatter.Rmd b/vignettes/splatter.Rmd
index 03eb98f35c1ec9b7494f15f0be590b1a49f53977..376ba8c104ecb87ddb4e36679b9b77b7e0f573b1 100644
--- a/vignettes/splatter.Rmd
+++ b/vignettes/splatter.Rmd
@@ -452,6 +452,27 @@ ggplot(comparison$PhenoData,
geom_point()
```
+## Comparing differences
+
+Sometimes instead of visually comparing datasets it may be more interesting to
+look at the differences between them. We can do this using the `diffSCESets`
+function. Similar to `compareSCESets` this function takes a list of `SCESet`
+objects but now we also specify one to be a reference. A series of similar plots
+are returned but instead of showing the overall distributions they demonstrate
+differences from the reference.
+
+```{r difference}
+difference <- diffSCESets(list(Splat = sim1, Simple = sim2), ref = "Simple")
+difference$Plots$Means
+```
+
+We also get a series of Quantile-Quantile plot that can be used to compare
+distributions.
+
+```{r difference-qq}
+difference$QQPlots$Means
+```
+
# Session information {-}
```{r sessionInfo}