From 81ed90d183776e7fc2857559734167c75fa751dd Mon Sep 17 00:00:00 2001
From: Luke Zappia <lazappi@users.noreply.github.com>
Date: Thu, 7 Sep 2017 16:48:55 +1000
Subject: [PATCH] Update SCE and comparison functions
---
NAMESPACE | 8 +-
R/SCE-functions.R | 76 +++----
R/compare.R | 261 ++++++++++++----------
man/addFeatureStats.Rd | 18 +-
man/addGeneLengths.Rd | 26 ++-
man/{compareSCESets.Rd => compareSCEs.Rd} | 22 +-
man/{diffSCESets.Rd => diffSCEs.Rd} | 38 ++--
man/makeCompPanel.Rd | 6 +-
man/makeDiffPanel.Rd | 6 +-
man/makeOverallPanel.Rd | 6 +-
man/summariseDiff.Rd | 8 +-
11 files changed, 248 insertions(+), 227 deletions(-)
rename man/{compareSCESets.Rd => compareSCEs.Rd} (80%)
rename man/{diffSCESets.Rd => diffSCEs.Rd} (71%)
diff --git a/NAMESPACE b/NAMESPACE
index 53c0acc..216436b 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -11,8 +11,8 @@ S3method(simpleEstimate,matrix)
S3method(splatEstimate,SingleCellExperiment)
S3method(splatEstimate,matrix)
export(addGeneLengths)
-export(compareSCESets)
-export(diffSCESets)
+export(compareSCEs)
+export(diffSCEs)
export(getParam)
export(getParams)
export(listSims)
@@ -45,10 +45,9 @@ exportClasses(LunParams)
exportClasses(SCDDParams)
exportClasses(SimpleParams)
exportClasses(SplatParams)
-importFrom(Biobase,"fData<-")
-importFrom(Biobase,fData)
importFrom(BiocParallel,SerialParam)
importFrom(BiocParallel,bplapply)
+importFrom(SingleCellExperiment,"cpm<-")
importFrom(SingleCellExperiment,SingleCellExperiment)
importFrom(SummarizedExperiment,"assays<-")
importFrom(SummarizedExperiment,"colData<-")
@@ -91,7 +90,6 @@ importFrom(methods,new)
importFrom(methods,slot)
importFrom(methods,slotNames)
importFrom(methods,validObject)
-importFrom(scater,"cpm<-")
importFrom(stats,dnbinom)
importFrom(stats,median)
importFrom(stats,model.matrix)
diff --git a/R/SCE-functions.R b/R/SCE-functions.R
index 2a92c35..a4b97d8 100644
--- a/R/SCE-functions.R
+++ b/R/SCE-functions.R
@@ -1,11 +1,11 @@
#' Add feature statistics
#'
-#' Add additional feature statistics to an SCESet object
+#' Add additional feature statistics to a SingleCellExperiment object
#'
-#' @param sce SCESet to add feature statistics to.
+#' @param sce SingleCellExperiment to add feature statistics to.
#' @param value the expression value to calculate statistics for. Options are
#' "counts", "cpm", "tpm" or "fpkm". The values need to exist in the
-#' given SCESet.
+#' given SingleCellExperiment.
#' @param log logical. Whether to take log2 before calculating statistics.
#' @param offset offset to add to avoid taking log of zero.
#' @param no.zeros logical. Whether to remove all zeros from each feature before
@@ -14,34 +14,38 @@
#' @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.
-#' UpperCamelCase is used to differentiate these columns from those added by
-#' \code{scater}.
+#' the \code{\link[SummarizedExperiment]{rowData}} 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 analysis packages.
#'
-#' @return SCESet with additional feature statistics
+#' @return SingleCellExperiment with additional feature statistics
#'
-#' @importFrom Biobase fData fData<-
+#' @importFrom SummarizedExperiment rowData rowData<-
addFeatureStats <- function(sce, value = c("counts", "cpm", "tpm", "fpkm"),
log = FALSE, offset = 1, no.zeros = FALSE) {
+ checkmate::assertClass(sce, "SingleCellExperiment")
+ checkmate::assertLogical(log)
+ checkmate::assertNumber(offset, lower = 0)
+ checkmate::assertLogical(no.zeros)
value <- match.arg(value)
switch(value,
counts = {
- values = scater::counts(sce)
+ values = SummarizedExperiment::assays(sce)$counts
suffix <- "Counts"
},
cpm = {
- values = scater::cpm(sce)
+ values = SummarizedExperiment::assays(sce)$cpm
suffix <- "CPM"
},
tpm = {
- values = scater::tpm(sce)
+ values = SummarizedExperiment::assays(sce)$tpm
suffix <- "TPM"
},
fpkm = {
- values = scater::fpkm(sce)
+ values = SummarizedExperiment::assays(sce)$fpkm
suffix <- "FPKM"
}
)
@@ -62,41 +66,43 @@ addFeatureStats <- function(sce, value = c("counts", "cpm", "tpm", "fpkm"),
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)
- fData(sce)[, cv.str] <- sqrt(fData(sce)[, var.str]) /
- fData(sce)[, mean.str]
- fData(sce)[, med.str] <- matrixStats::rowMedians(values, na.rm = TRUE)
- fData(sce)[, mad.str] <- matrixStats::rowMads(values, na.rm = TRUE)
+ rowData(sce)[, mean.str] <- rowMeans(values, na.rm = TRUE)
+ rowData(sce)[, var.str] <- matrixStats::rowVars(values, na.rm = TRUE)
+ rowData(sce)[, cv.str] <- sqrt(rowData(sce)[, var.str]) /
+ rowData(sce)[, mean.str]
+ rowData(sce)[, med.str] <- matrixStats::rowMedians(values, na.rm = TRUE)
+ rowData(sce)[, mad.str] <- matrixStats::rowMads(values, na.rm = TRUE)
return(sce)
}
#' Add gene lengths
#'
-#' Add gene lengths to an SCESet object
+#' Add gene lengths to an SingleCellExperiment object
#'
-#' @param sce SCESet to add gene lengths to.
+#' @param sce SingleCellExperiment to add gene lengths to.
#' @param method Method to use for creating lengths.
#' @param loc Location parameter for the generate method.
#' @param scale Scale parameter for the generate method.
#' @param lengths Vector of lengths for the sample method.
#'
#' @details
-#' This function adds simulated gene lengths to the \code{fData} slot of an
-#' \code{SCESet} object that can be used for calculating length normalised
-#' expression values such as TPM or FPKM. The \code{generate} simulates lengths
-#' using a (rounded) log-normal distribution, with the default \code{loc} and
-#' \code{scale} parameters based on human coding genes. Alternatively the
-#' \code{sample} method can be used which randomly samples lengths (with
-#' replacement) from a supplied vector.
+#' This function adds simulated gene lengths to the
+#' \code{\link[SummarizedExperiment]{rowData}} slot of a
+#' \code{\link[SingleCellExperiment]{SingleCellExperiment}} object that can be
+#' used for calculating length normalised expression values such as TPM or FPKM.
+#' The \code{generate} method simulates lengths using a (rounded) log-normal
+#' distribution, with the default \code{loc} and \code{scale} parameters based
+#' on human protein-coding genes. Alternatively the \code{sample} method can be
+#' used which randomly samples lengths (with replacement) from a supplied
+#' vector.
#'
-#' @return SCESet with added gene lengths
+#' @return SingleCellExperiment with added gene lengths
#' @examples
#' # Default generate method
#' sce <- simpleSimulate()
#' sce <- addGeneLengths(sce)
-#' head(fData(sce))
+#' head(rowData(sce))
#' # Sample method (human coding genes)
#' \dontrun{
#' library(TxDb.Hsapiens.UCSC.hg19.knownGene)
@@ -113,7 +119,7 @@ addGeneLengths <- function(sce, method = c("generate", "sample"), loc = 7.9,
scale = 0.7, lengths = NULL) {
method <- match.arg(method)
- checkmate::assertClass(sce, "SCESet")
+ checkmate::assertClass(sce, "SingleCellExperiment")
checkmate::assertNumber(loc)
checkmate::assertNumber(scale, lower = 0)
checkmate::assertNumeric(lengths, lower = 0, null.ok = TRUE)
@@ -132,13 +138,7 @@ addGeneLengths <- function(sce, method = c("generate", "sample"), loc = 7.9,
}
)
- fData(sce)$Length <- sim.lengths
+ rowData(sce)$Length <- sim.lengths
return(sce)
}
-
-#txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene
-#tx.lens <- GenomicFeatures::transcriptLengths(txdb, with.cds_len = TRUE)
-#tx.lens <- tx.lens[tx.lens$cds_len > 0, ]
-#gene.lens <- max(IRanges::splitAsList(tx.lens$tx_len, tx.lens$gene_id))
-#lens <- rlnorm(length(gene.lens), meanlog = 7.9, sdlog = 0.7)
diff --git a/R/compare.R b/R/compare.R
index 45af803..6739fb9 100644
--- a/R/compare.R
+++ b/R/compare.R
@@ -1,9 +1,10 @@
-#' Compare SCESet objects
+#' Compare SingleCellExperiment objects
#'
-#' Combine the data from several SCESet objects and produce some basic plots
-#' comparing them.
+#' Combine the data from several SingleCellExperiment objects and produce some
+#' basic plots comparing them.
#'
-#' @param sces named list of SCESet objects to combine and compare.
+#' @param sces named list of SingleCellExperiment objects to combine and
+#' compare.
#' @param point.size size of points in scatter plots.
#' @param point.alpha opacity of points in scatter plots.
#' @param fits whether to include fits in scatter plots.
@@ -14,8 +15,9 @@
#'
#' \describe{
#' \item{\code{FeatureData}}{Combined feature data from the provided
-#' SCESets.}
-#' \item{\code{PhenoData}}{Combined pheno data from the provided SCESets.}
+#' SingleCellExperiments.}
+#' \item{\code{PhenoData}}{Combined pheno data from the provided
+#' SingleCellExperiments.}
#' \item{\code{Plots}}{Comparison plots
#' \describe{
#' \item{\code{Means}}{Boxplot of mean distribution.}
@@ -44,19 +46,19 @@
#' @examples
#' sim1 <- splatSimulate(nGenes = 1000, batchCells = 20)
#' sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
-#' comparison <- compareSCESets(list(Splat = sim1, Simple = sim2))
+#' comparison <- compareSCEs(list(Splat = sim1, Simple = sim2))
#' names(comparison)
#' names(comparison$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<-
+#' @importFrom SingleCellExperiment cpm<-
#' @export
-compareSCESets <- function(sces, point.size = 0.1, point.alpha = 0.1,
- fits = TRUE, colours = NULL) {
+compareSCEs <- 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::assertList(sces, types = "SingleCellExperiment",
+ 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)
@@ -70,51 +72,53 @@ compareSCESets <- function(sces, point.size = 0.1, point.alpha = 0.1,
for (name in names(sces)) {
sce <- sces[[name]]
- fData(sce)$Dataset <- name
- pData(sce)$Dataset <- name
+ rowData(sce)$Dataset <- name
+ colData(sce)$Dataset <- name
sce <- scater::calculateQCMetrics(sce)
- cpm(sce) <- edgeR::cpm(counts(sce))
+ cpm(sce) <- scater::calculateCPM(sce, use.size.factors = FALSE)
sce <- addFeatureStats(sce, "counts")
sce <- addFeatureStats(sce, "cpm")
sce <- addFeatureStats(sce, "cpm", log = TRUE)
+ colData(sce)$PctZero <- 100 * (1 - colData(sce)$total_features /
+ nrow(sce))
sces[[name]] <- sce
}
- fData.all <- fData(sces[[1]])
- pData.all <- pData(sces[[1]])
+ features <- rowData(sces[[1]])
+ cells <- colData(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))
+ features <- rbindMatched(features, rowData(sce))
+ cells <- rbindMatched(cells, colData(sce))
}
}
- fData.all$Dataset <- factor(fData.all$Dataset, levels = names(sces))
- pData.all$Dataset <- factor(pData.all$Dataset, levels = names(sces))
+ features$Dataset <- factor(features$Dataset, levels = names(sces))
+ cells$Dataset <- factor(cells$Dataset, levels = names(sces))
+ features <- data.frame(features)
+ cells <- data.frame(cells)
- means <- ggplot(fData.all,
+ means <- ggplot(features,
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,
+ vars <- ggplot(features,
aes_string(x = "Dataset", y = "VarLogCPM",
colour = "Dataset")) +
- #geom_violin(draw_quantiles = c(0.25, 0.5, 0.75)) +
geom_boxplot() +
scale_colour_manual(values = colours) +
ylab(expression(paste("Variance ", log[2], "(CPM + 1)"))) +
ggtitle("Distribution of variance") +
theme_minimal()
- mean.var <- ggplot(fData.all,
+ mean.var <- ggplot(features,
aes_string(x = "MeanLogCPM", y = "VarLogCPM",
colour = "Dataset", fill = "Dataset")) +
geom_point(size = point.size, alpha = point.alpha) +
@@ -125,7 +129,7 @@ compareSCESets <- function(sces, point.size = 0.1, point.alpha = 0.1,
ggtitle("Mean-variance relationship") +
theme_minimal()
- libs <- ggplot(pData.all,
+ libs <- ggplot(cells,
aes_string(x = "Dataset", y = "total_counts",
colour = "Dataset")) +
geom_boxplot() +
@@ -135,8 +139,8 @@ compareSCESets <- function(sces, point.size = 0.1, point.alpha = 0.1,
ggtitle("Distribution of library sizes") +
theme_minimal()
- z.gene <- ggplot(fData.all,
- aes_string(x = "Dataset", y = "pct_dropout",
+ z.gene <- ggplot(features,
+ aes_string(x = "Dataset", y = "pct_dropout_counts",
colour = "Dataset")) +
geom_boxplot() +
scale_y_continuous(limits = c(0, 100)) +
@@ -145,8 +149,8 @@ compareSCESets <- function(sces, point.size = 0.1, point.alpha = 0.1,
ggtitle("Distribution of zeros per gene") +
theme_minimal()
- z.cell <- ggplot(pData.all,
- aes_string(x = "Dataset", y = "pct_dropout",
+ z.cell <- ggplot(cells,
+ aes_string(x = "Dataset", y = "PctZero",
colour = "Dataset")) +
geom_boxplot() +
scale_y_continuous(limits = c(0, 100)) +
@@ -155,8 +159,8 @@ compareSCESets <- function(sces, point.size = 0.1, point.alpha = 0.1,
ggtitle("Distribution of zeros per cell") +
theme_minimal()
- mean.zeros <- ggplot(fData.all,
- aes_string(x = "MeanCounts", y = "pct_dropout",
+ mean.zeros <- ggplot(features,
+ aes_string(x = "MeanCounts", y = "pct_dropout_counts",
colour = "Dataset", fill = "Dataset")) +
geom_point(size = point.size, alpha = point.alpha) +
scale_x_log10(labels = scales::comma) +
@@ -172,8 +176,8 @@ compareSCESets <- function(sces, point.size = 0.1, point.alpha = 0.1,
mean.zeros <- mean.zeros + geom_smooth()
}
- comparison <- list(FeatureData = fData.all,
- PhenoData = pData.all,
+ comparison <- list(FeatureData = features,
+ PhenoData = cells,
Plots = list(Means = means,
Variances = vars,
MeanVar = mean.var,
@@ -185,13 +189,15 @@ compareSCESets <- function(sces, point.size = 0.1, point.alpha = 0.1,
return(comparison)
}
-#' Diff SCESet objects
+#' Diff SingleCellExperiment objects
#'
-#' Combine the data from several SCESet objects and produce some basic plots
-#' comparing them to a reference.
+#' Combine the data from several SingleCellExperiment 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 sces named list of SingleCellExperiment objects to combine and
+#' compare.
+#' @param ref string giving the name of the SingleCellExperiment 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.
@@ -199,19 +205,21 @@ compareSCESets <- function(sces, point.size = 0.1, point.alpha = 0.1,
#'
#' @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.
+#' This function aims to look at the differences between a reference
+#' SingleCellExperiment and one or more others. It requires each
+#' SingleCellExperiment 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{Reference}}{The SingleCellExperiment used as the reference.}
#' \item{\code{FeatureData}}{Combined feature data from the provided
-#' SCESets.}
-#' \item{\code{PhenoData}}{Combined pheno data from the provided SCESets.}
+#' SingleCellExperiments.}
+#' \item{\code{PhenoData}}{Combined pheno data from the provided
+#' SingleCellExperiments.}
#' \item{\code{Plots}}{Difference plots
#' \describe{
#' \item{\code{Means}}{Boxplot of mean differences.}
@@ -251,26 +259,26 @@ compareSCESets <- function(sces, point.size = 0.1, point.alpha = 0.1,
#' @examples
#' sim1 <- splatSimulate(nGenes = 1000, batchCells = 20)
#' sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
-#' difference <- diffSCESets(list(Splat = sim1, Simple = sim2), ref = "Simple")
+#' difference <- diffSCEs(list(Splat = sim1, Simple = sim2), ref = "Simple")
#' names(difference)
#' names(difference$Plots)
#' @importFrom ggplot2 ggplot aes_string geom_point geom_boxplot xlab ylab
#' ggtitle theme_minimal geom_hline geom_abline scale_colour_manual
#' scale_fill_manual
-#' @importFrom scater cpm<-
+#' @importFrom SingleCellExperiment cpm<-
#' @export
-diffSCESets <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
- fits = TRUE, colours = NULL) {
+diffSCEs <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
+ fits = TRUE, colours = NULL) {
- checkmate::assertList(sces, types = "SCESet", any.missing = FALSE,
- min.len = 2, names = "unique")
+ checkmate::assertList(sces, types = "SingleCellExperiment",
+ any.missing = FALSE, min.len = 2, names = "unique")
checkmate::assertString(ref)
checkmate::assertNumber(point.size, finite = TRUE)
checkmate::assertNumber(point.alpha, lower = 0, upper = 1)
checkmate::assertLogical(fits, any.missing = FALSE, len = 1)
if (!(ref %in% names(sces))) {
- stop("'ref' must be the name of an SCESet in 'sces'")
+ stop("'ref' must be the name of a SingleCellExperiment in 'sces'")
} else {
ref.idx <- which(names(sces) == ref)
}
@@ -290,49 +298,54 @@ diffSCESets <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
if (!identical(dim(sce), ref.dim)) {
stop("SCESets must have the same dimensions")
}
- fData(sce)$Dataset <- name
- pData(sce)$Dataset <- name
+ rowData(sce)$Dataset <- name
+ colData(sce)$Dataset <- name
sce <- scater::calculateQCMetrics(sce)
- cpm(sce) <- edgeR::cpm(counts(sce))
+ cpm(sce) <- scater::calculateCPM(sce, use.size.factors = FALSE)
sce <- addFeatureStats(sce, "counts")
sce <- addFeatureStats(sce, "cpm", log = TRUE)
+ colData(sce)$PctZero <- 100 * (1 - colData(sce)$total_features /
+ nrow(sce))
sces[[name]] <- sce
}
ref.sce <- sces[[ref]]
- ref.means <- sort(fData(ref.sce)$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.means <- sort(rowData(ref.sce)$MeanLogCPM)
+ ref.vars <- sort(rowData(ref.sce)$VarLogCPM)
+ ref.libs <- sort(colData(ref.sce)$total_counts)
+ ref.z.gene <- sort(rowData(ref.sce)$pct_dropout_counts)
+ ref.z.cell <- sort(colData(ref.sce)$PctZero)
- ref.rank.ord <- order(fData(ref.sce)$exprs_rank)
- ref.vars.rank <- fData(ref.sce)$VarLogCPM[ref.rank.ord]
- ref.z.gene.rank <- fData(ref.sce)$pct_dropout[ref.rank.ord]
+ ref.rank.ord <- order(rowData(ref.sce)$rank_counts)
+ ref.vars.rank <- rowData(ref.sce)$VarLogCPM[ref.rank.ord]
+ ref.z.gene.rank <- rowData(ref.sce)$pct_dropout_counts[ref.rank.ord]
for (name in names(sces)) {
sce <- sces[[name]]
- fData(sce)$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]
+ rowData(sce)$RefRankMeanLogCPM <- ref.means[
+ rank(rowData(sce)$MeanLogCPM)]
+ rowData(sce)$RankDiffMeanLogCPM <- rowData(sce)$MeanLogCPM -
+ rowData(sce)$RefRankMeanLogCPM
+ rowData(sce)$RefRankVarLogCPM <- ref.vars[rank(rowData(sce)$VarLogCPM)]
+ rowData(sce)$RankDiffVarLogCPM <- rowData(sce)$VarLogCPM -
+ rowData(sce)$RefRankVarLogCPM
+ colData(sce)$RefRankLibSize <- ref.libs[rank(colData(sce)$total_counts)]
+ colData(sce)$RankDiffLibSize <- colData(sce)$total_counts -
+ colData(sce)$RefRankLibSize
+ rowData(sce)$RefRankZeros <- ref.z.gene[rank(
+ rowData(sce)$pct_dropout_counts)]
+ rowData(sce)$RankDiffZeros <- rowData(sce)$pct_dropout_counts -
+ rowData(sce)$RefRankZeros
+ colData(sce)$RefRankZeros <- ref.z.cell[rank(
+ colData(sce)$PctZero)]
+ colData(sce)$RankDiffZeros <- colData(sce)$PctZero -
+ colData(sce)$RefRankZeros
+
+ rowData(sce)$MeanRankVarDiff <- rowData(sce)$VarLogCPM -
+ ref.vars.rank[rowData(sce)$rank_counts]
+ rowData(sce)$MeanRankZerosDiff <- rowData(sce)$pct_dropout_counts -
+ ref.z.gene.rank[rowData(sce)$rank_counts]
sces[[name]] <- sce
}
@@ -340,21 +353,23 @@ diffSCESets <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
ref.sce <- sces[[ref]]
sces[[ref]] <- NULL
- fData.all <- fData(sces[[1]])
- pData.all <- pData(sces[[1]])
+ features <- rowData(sces[[1]])
+ cells <- colData(sces[[1]])
if (length(sces) > 1) {
for (name in names(sces)[-1]) {
sce <- sces[[name]]
- fData.all <- rbindMatched(fData.all, fData(sce))
- pData.all <- rbindMatched(pData.all, pData(sce))
+ features <- rbindMatched(features, rowData(sce))
+ cells <- rbindMatched(cells, colData(sce))
}
}
- fData.all$Dataset <- factor(fData.all$Dataset, levels = names(sces))
- pData.all$Dataset <- factor(pData.all$Dataset, levels = names(sces))
+ features$Dataset <- factor(features$Dataset, levels = names(sces))
+ cells$Dataset <- factor(cells$Dataset, levels = names(sces))
+ features <- data.frame(features)
+ cells <- data.frame(cells)
- means <- ggplot(fData.all,
+ means <- ggplot(features,
aes_string(x = "Dataset", y = "RankDiffMeanLogCPM",
colour = "Dataset")) +
geom_hline(yintercept = 0, colour = "red") +
@@ -364,7 +379,7 @@ diffSCESets <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
ggtitle("Difference in mean expression") +
theme_minimal()
- vars <- ggplot(fData.all,
+ vars <- ggplot(features,
aes_string(x = "Dataset", y = "RankDiffVarLogCPM",
colour = "Dataset")) +
geom_hline(yintercept = 0, colour = "red") +
@@ -375,8 +390,8 @@ diffSCESets <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
ggtitle("Difference in variance") +
theme_minimal()
- mean.var <- ggplot(fData.all,
- aes_string(x = "exprs_rank", y = "MeanRankVarDiff",
+ mean.var <- ggplot(features,
+ aes_string(x = "rank_counts", y = "MeanRankVarDiff",
colour = "Dataset", fill = "Dataset")) +
geom_hline(yintercept = 0, colour = "red") +
geom_point(size = point.size, alpha = point.alpha) +
@@ -388,7 +403,7 @@ diffSCESets <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
ggtitle("Difference in mean-variance relationship") +
theme_minimal()
- libs <- ggplot(pData.all,
+ libs <- ggplot(cells,
aes_string(x = "Dataset", y = "RankDiffLibSize",
colour = "Dataset")) +
geom_hline(yintercept = 0, colour = "red") +
@@ -398,7 +413,7 @@ diffSCESets <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
ggtitle("Difference in library sizes") +
theme_minimal()
- z.gene <- ggplot(fData.all,
+ z.gene <- ggplot(features,
aes_string(x = "Dataset", y = "RankDiffZeros",
colour = "Dataset")) +
geom_hline(yintercept = 0, colour = "red") +
@@ -408,7 +423,7 @@ diffSCESets <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
ggtitle("Difference in zeros per gene") +
theme_minimal()
- z.cell <- ggplot(pData.all,
+ z.cell <- ggplot(cells,
aes_string(x = "Dataset", y = "RankDiffZeros",
colour = "Dataset")) +
geom_hline(yintercept = 0, colour = "red") +
@@ -418,8 +433,8 @@ diffSCESets <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
ggtitle("Difference in zeros per cell") +
theme_minimal()
- mean.zeros <- ggplot(fData.all,
- aes_string(x = "exprs_rank", y = "MeanRankZerosDiff",
+ mean.zeros <- ggplot(features,
+ aes_string(x = "rank_counts", y = "MeanRankZerosDiff",
colour = "Dataset", fill = "Dataset")) +
geom_hline(yintercept = 0, colour = "red") +
geom_point(size = point.size, alpha = point.alpha) +
@@ -430,7 +445,7 @@ diffSCESets <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
ggtitle("Difference in mean-zeros relationship") +
theme_minimal()
- means.qq <- ggplot(fData.all,
+ means.qq <- ggplot(features,
aes_string(x = "RefRankMeanLogCPM", y = "MeanLogCPM",
colour = "Dataset")) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
@@ -441,7 +456,7 @@ diffSCESets <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
ggtitle("Ranked means") +
theme_minimal()
- vars.qq <- ggplot(fData.all,
+ vars.qq <- ggplot(features,
aes_string(x = "RefRankVarLogCPM", y = "VarLogCPM",
colour = "Dataset")) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
@@ -452,7 +467,7 @@ diffSCESets <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
ggtitle("Ranked variances") +
theme_minimal()
- libs.qq <- ggplot(pData.all,
+ libs.qq <- ggplot(cells,
aes_string(x = "RefRankLibSize", y = "total_counts",
colour = "Dataset")) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
@@ -463,8 +478,8 @@ diffSCESets <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
ggtitle("Ranked library sizes") +
theme_minimal()
- z.gene.qq <- ggplot(fData.all,
- aes_string(x = "RefRankZeros", y = "pct_dropout",
+ z.gene.qq <- ggplot(features,
+ aes_string(x = "RefRankZeros", y = "pct_dropout_counts",
colour = "Dataset")) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
geom_point(size = point.size, alpha = point.alpha) +
@@ -474,8 +489,8 @@ diffSCESets <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
ggtitle("Ranked percentage zeros per gene") +
theme_minimal()
- z.cell.qq <- ggplot(pData.all,
- aes_string(x = "RefRankZeros", y = "pct_dropout",
+ z.cell.qq <- ggplot(cells,
+ aes_string(x = "RefRankZeros", y = "PctZero",
colour = "Dataset")) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
geom_point(size = point.size, alpha = point.alpha) +
@@ -491,8 +506,8 @@ diffSCESets <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
}
comparison <- list(Reference = ref.sce,
- FeatureData = fData.all,
- PhenoData = pData.all,
+ FeatureData = features,
+ PhenoData = cells,
Plots = list(Means = means,
Variances = vars,
MeanVar = mean.var,
@@ -511,9 +526,9 @@ diffSCESets <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
#' Make comparison panel
#'
-#' Combine the plots from \code{compareSCESets} into a single panel.
+#' Combine the plots from \code{compareSCEs} into a single panel.
#'
-#' @param comp list returned by \code{\link{compareSCESets}}.
+#' @param comp list returned by \code{\link{compareSCEs}}.
#' @param title title for the panel.
#' @param labels vector of labels for each of the seven plots.
#'
@@ -523,7 +538,7 @@ diffSCESets <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
#' \dontrun{
#' sim1 <- splatSimulate(nGenes = 1000, batchCells = 20)
#' sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
-#' comparison <- compareSCESets(list(Splat = sim1, Simple = sim2))
+#' comparison <- compareSCEs(list(Splat = sim1, Simple = sim2))
#' panel <- makeCompPanel(comparison)
#' }
#'
@@ -595,9 +610,9 @@ makeCompPanel <- function(comp, title = "Comparison",
#' Make difference panel
#'
-#' Combine the plots from \code{diffSCESets} into a single panel.
+#' Combine the plots from \code{diffSCEs} into a single panel.
#'
-#' @param diff list returned by \code{\link{diffSCESets}}.
+#' @param diff list returned by \code{\link{diffSCEs}}.
#' @param title title for the panel.
#' @param labels vector of labels for each of the seven sections.
#'
@@ -607,7 +622,7 @@ makeCompPanel <- function(comp, title = "Comparison",
#' \dontrun{
#' sim1 <- splatSimulate(nGenes = 1000, batchCells = 20)
#' sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
-#' difference <- diffSCESets(list(Splat = sim1, Simple = sim2), ref = "Simple")
+#' difference <- diffSCEs(list(Splat = sim1, Simple = sim2), ref = "Simple")
#' panel <- makeDiffPanel(difference)
#' }
#'
@@ -685,11 +700,11 @@ makeDiffPanel <- function(diff, title = "Difference comparison",
#' Make overall panel
#'
-#' Combine the plots from \code{compSCESets} and \code{diffSCESets} into a
+#' Combine the plots from \code{compSCEs} and \code{diffSCEs} into a
#' single panel.
#'
-#' @param comp list returned by \code{\link{compareSCESets}}.
-#' @param diff list returned by \code{\link{diffSCESets}}.
+#' @param comp list returned by \code{\link{compareSCEs}}.
+#' @param diff list returned by \code{\link{diffSCEs}}.
#' @param title title for the panel.
#' @param row.labels vector of labels for each of the seven rows.
#'
@@ -788,19 +803,19 @@ makeOverallPanel <- function(comp, diff, title = "Overall comparison",
return(panel)
}
-#' Summarise diffSCESets
+#' Summarise diffSCESs
#'
-#' Summarise the results of \code{\link{diffSCESets}}. Calculates the Median
+#' Summarise the results of \code{\link{diffSCEs}}. Calculates the Median
#' Absolute Deviation (MAD), Mean Absolute Error (MAE) and Root Mean Squared
#' Error (RMSE) for the various properties and ranks them.
#'
-#' @param diff Output from \code{\link{diffSCESets}}
+#' @param diff Output from \code{\link{diffSCEs}}
#'
#' @return data.frame with MADs, MAEs, RMSEs, scaled statistics and ranks
#' @examples
#' sim1 <- splatSimulate(nGenes = 1000, batchCells = 20)
#' sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
-#' difference <- diffSCESets(list(Splat = sim1, Simple = sim2), ref = "Simple")
+#' difference <- diffSCEs(list(Splat = sim1, Simple = sim2), ref = "Simple")
#' summary <- summariseDiff(difference)
#' head(summary)
#' @export
diff --git a/man/addFeatureStats.Rd b/man/addFeatureStats.Rd
index f4f72ef..4edd20d 100644
--- a/man/addFeatureStats.Rd
+++ b/man/addFeatureStats.Rd
@@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/SCESet-functions.R
+% Please edit documentation in R/SCE-functions.R
\name{addFeatureStats}
\alias{addFeatureStats}
\title{Add feature statistics}
@@ -8,11 +8,11 @@ addFeatureStats(sce, value = c("counts", "cpm", "tpm", "fpkm"), log = FALSE,
offset = 1, no.zeros = FALSE)
}
\arguments{
-\item{sce}{SCESet to add feature statistics to.}
+\item{sce}{SingleCellExperiment to add feature statistics to.}
\item{value}{the expression value to calculate statistics for. Options are
"counts", "cpm", "tpm" or "fpkm". The values need to exist in the
-given SCESet.}
+given SingleCellExperiment.}
\item{log}{logical. Whether to take log2 before calculating statistics.}
@@ -22,16 +22,16 @@ given SCESet.}
calculating statistics.}
}
\value{
-SCESet with additional feature statistics
+SingleCellExperiment with additional feature statistics
}
\description{
-Add additional feature statistics to an SCESet object
+Add additional feature statistics to a SingleCellExperiment 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.
-UpperCamelCase is used to differentiate these columns from those added by
-\code{scater}.
+the \code{\link[SummarizedExperiment]{rowData}} 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 analysis packages.
}
diff --git a/man/addGeneLengths.Rd b/man/addGeneLengths.Rd
index dd760ad..3cfbc60 100644
--- a/man/addGeneLengths.Rd
+++ b/man/addGeneLengths.Rd
@@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/SCESet-functions.R
+% Please edit documentation in R/SCE-functions.R
\name{addGeneLengths}
\alias{addGeneLengths}
\title{Add gene lengths}
@@ -8,7 +8,7 @@ addGeneLengths(sce, method = c("generate", "sample"), loc = 7.9,
scale = 0.7, lengths = NULL)
}
\arguments{
-\item{sce}{SCESet to add gene lengths to.}
+\item{sce}{SingleCellExperiment to add gene lengths to.}
\item{method}{Method to use for creating lengths.}
@@ -19,25 +19,27 @@ addGeneLengths(sce, method = c("generate", "sample"), loc = 7.9,
\item{lengths}{Vector of lengths for the sample method.}
}
\value{
-SCESet with added gene lengths
+SingleCellExperiment with added gene lengths
}
\description{
-Add gene lengths to an SCESet object
+Add gene lengths to an SingleCellExperiment object
}
\details{
-This function adds simulated gene lengths to the \code{fData} slot of an
-\code{SCESet} object that can be used for calculating length normalised
-expression values such as TPM or FPKM. The \code{generate} simulates lengths
-using a (rounded) log-normal distribution, with the default \code{loc} and
-\code{scale} parameters based on human coding genes. Alternatively the
-\code{sample} method can be used which randomly samples lengths (with
-replacement) from a supplied vector.
+This function adds simulated gene lengths to the
+\code{\link[SummarizedExperiment]{rowData}} slot of a
+\code{\link[SingleCellExperiment]{SingleCellExperiment}} object that can be
+used for calculating length normalised expression values such as TPM or FPKM.
+The \code{generate} method simulates lengths using a (rounded) log-normal
+distribution, with the default \code{loc} and \code{scale} parameters based
+on human protein-coding genes. Alternatively the \code{sample} method can be
+used which randomly samples lengths (with replacement) from a supplied
+vector.
}
\examples{
# Default generate method
sce <- simpleSimulate()
sce <- addGeneLengths(sce)
-head(fData(sce))
+head(rowData(sce))
# Sample method (human coding genes)
\dontrun{
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
diff --git a/man/compareSCESets.Rd b/man/compareSCEs.Rd
similarity index 80%
rename from man/compareSCESets.Rd
rename to man/compareSCEs.Rd
index 4c4393c..711a2be 100644
--- a/man/compareSCESets.Rd
+++ b/man/compareSCEs.Rd
@@ -1,14 +1,15 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/compare.R
-\name{compareSCESets}
-\alias{compareSCESets}
-\title{Compare SCESet objects}
+\name{compareSCEs}
+\alias{compareSCEs}
+\title{Compare SingleCellExperiment objects}
\usage{
-compareSCESets(sces, point.size = 0.1, point.alpha = 0.1, fits = TRUE,
+compareSCEs(sces, point.size = 0.1, point.alpha = 0.1, fits = TRUE,
colours = NULL)
}
\arguments{
-\item{sces}{named list of SCESet objects to combine and compare.}
+\item{sces}{named list of SingleCellExperiment objects to combine and
+compare.}
\item{point.size}{size of points in scatter plots.}
@@ -22,16 +23,17 @@ compareSCESets(sces, point.size = 0.1, point.alpha = 0.1, fits = TRUE,
List containing the combined datasets and plots.
}
\description{
-Combine the data from several SCESet objects and produce some basic plots
-comparing them.
+Combine the data from several SingleCellExperiment objects and produce some
+basic plots comparing them.
}
\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.}
+ SingleCellExperiments.}
+ \item{\code{PhenoData}}{Combined pheno data from the provided
+ SingleCellExperiments.}
\item{\code{Plots}}{Comparison plots
\describe{
\item{\code{Means}}{Boxplot of mean distribution.}
@@ -59,7 +61,7 @@ using \code{\link[ggplot2]{ggplot}}.
\examples{
sim1 <- splatSimulate(nGenes = 1000, batchCells = 20)
sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
-comparison <- compareSCESets(list(Splat = sim1, Simple = sim2))
+comparison <- compareSCEs(list(Splat = sim1, Simple = sim2))
names(comparison)
names(comparison$Plots)
}
diff --git a/man/diffSCESets.Rd b/man/diffSCEs.Rd
similarity index 71%
rename from man/diffSCESets.Rd
rename to man/diffSCEs.Rd
index 7b459ca..a24a5c2 100644
--- a/man/diffSCESets.Rd
+++ b/man/diffSCEs.Rd
@@ -1,16 +1,18 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/compare.R
-\name{diffSCESets}
-\alias{diffSCESets}
-\title{Diff SCESet objects}
+\name{diffSCEs}
+\alias{diffSCEs}
+\title{Diff SingleCellExperiment objects}
\usage{
-diffSCESets(sces, ref, point.size = 0.1, point.alpha = 0.1, fits = TRUE,
+diffSCEs(sces, ref, point.size = 0.1, point.alpha = 0.1, fits = TRUE,
colours = NULL)
}
\arguments{
-\item{sces}{named list of SCESet objects to combine and compare.}
+\item{sces}{named list of SingleCellExperiment objects to combine and
+compare.}
-\item{ref}{string giving the name of the SCESet to use as the reference}
+\item{ref}{string giving the name of the SingleCellExperiment to use as the
+reference}
\item{point.size}{size of points in scatter plots.}
@@ -24,23 +26,25 @@ diffSCESets(sces, ref, point.size = 0.1, point.alpha = 0.1, fits = TRUE,
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.
+Combine the data from several SingleCellExperiment 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.
+This function aims to look at the differences between a reference
+SingleCellExperiment and one or more others. It requires each
+SingleCellExperiment 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{Reference}}{The SingleCellExperiment used as the reference.}
\item{\code{FeatureData}}{Combined feature data from the provided
- SCESets.}
- \item{\code{PhenoData}}{Combined pheno data from the provided SCESets.}
+ SingleCellExperiments.}
+ \item{\code{PhenoData}}{Combined pheno data from the provided
+ SingleCellExperiments.}
\item{\code{Plots}}{Difference plots
\describe{
\item{\code{Means}}{Boxplot of mean differences.}
@@ -79,7 +83,7 @@ using \code{\link[ggplot2]{ggplot}}.
\examples{
sim1 <- splatSimulate(nGenes = 1000, batchCells = 20)
sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
-difference <- diffSCESets(list(Splat = sim1, Simple = sim2), ref = "Simple")
+difference <- diffSCEs(list(Splat = sim1, Simple = sim2), ref = "Simple")
names(difference)
names(difference$Plots)
}
diff --git a/man/makeCompPanel.Rd b/man/makeCompPanel.Rd
index 7340fa2..c91b324 100644
--- a/man/makeCompPanel.Rd
+++ b/man/makeCompPanel.Rd
@@ -9,7 +9,7 @@ makeCompPanel(comp, title = "Comparison", labels = c("Means", "Variance",
"Zeros per cell", "Mean-zeros relationship"))
}
\arguments{
-\item{comp}{list returned by \code{\link{compareSCESets}}.}
+\item{comp}{list returned by \code{\link{compareSCEs}}.}
\item{title}{title for the panel.}
@@ -19,13 +19,13 @@ makeCompPanel(comp, title = "Comparison", labels = c("Means", "Variance",
Combined panel plot
}
\description{
-Combine the plots from \code{compareSCESets} into a single panel.
+Combine the plots from \code{compareSCEs} into a single panel.
}
\examples{
\dontrun{
sim1 <- splatSimulate(nGenes = 1000, batchCells = 20)
sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
-comparison <- compareSCESets(list(Splat = sim1, Simple = sim2))
+comparison <- compareSCEs(list(Splat = sim1, Simple = sim2))
panel <- makeCompPanel(comparison)
}
diff --git a/man/makeDiffPanel.Rd b/man/makeDiffPanel.Rd
index 0712f36..a0239a8 100644
--- a/man/makeDiffPanel.Rd
+++ b/man/makeDiffPanel.Rd
@@ -9,7 +9,7 @@ makeDiffPanel(diff, title = "Difference comparison", labels = c("Means",
"Mean-variance relationship", "Mean-zeros relationship"))
}
\arguments{
-\item{diff}{list returned by \code{\link{diffSCESets}}.}
+\item{diff}{list returned by \code{\link{diffSCEs}}.}
\item{title}{title for the panel.}
@@ -19,13 +19,13 @@ makeDiffPanel(diff, title = "Difference comparison", labels = c("Means",
Combined panel plot
}
\description{
-Combine the plots from \code{diffSCESets} into a single panel.
+Combine the plots from \code{diffSCEs} into a single panel.
}
\examples{
\dontrun{
sim1 <- splatSimulate(nGenes = 1000, batchCells = 20)
sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
-difference <- diffSCESets(list(Splat = sim1, Simple = sim2), ref = "Simple")
+difference <- diffSCEs(list(Splat = sim1, Simple = sim2), ref = "Simple")
panel <- makeDiffPanel(difference)
}
diff --git a/man/makeOverallPanel.Rd b/man/makeOverallPanel.Rd
index a37119d..51b219b 100644
--- a/man/makeOverallPanel.Rd
+++ b/man/makeOverallPanel.Rd
@@ -10,9 +10,9 @@ makeOverallPanel(comp, diff, title = "Overall comparison",
"Mean-zeros relationship"))
}
\arguments{
-\item{comp}{list returned by \code{\link{compareSCESets}}.}
+\item{comp}{list returned by \code{\link{compareSCEs}}.}
-\item{diff}{list returned by \code{\link{diffSCESets}}.}
+\item{diff}{list returned by \code{\link{diffSCEs}}.}
\item{title}{title for the panel.}
@@ -22,7 +22,7 @@ makeOverallPanel(comp, diff, title = "Overall comparison",
Combined panel plot
}
\description{
-Combine the plots from \code{compSCESets} and \code{diffSCESets} into a
+Combine the plots from \code{compSCEs} and \code{diffSCEs} into a
single panel.
}
\examples{
diff --git a/man/summariseDiff.Rd b/man/summariseDiff.Rd
index cd6cb62..10b3b06 100644
--- a/man/summariseDiff.Rd
+++ b/man/summariseDiff.Rd
@@ -2,25 +2,25 @@
% Please edit documentation in R/compare.R
\name{summariseDiff}
\alias{summariseDiff}
-\title{Summarise diffSCESets}
+\title{Summarise diffSCESs}
\usage{
summariseDiff(diff)
}
\arguments{
-\item{diff}{Output from \code{\link{diffSCESets}}}
+\item{diff}{Output from \code{\link{diffSCEs}}}
}
\value{
data.frame with MADs, MAEs, RMSEs, scaled statistics and ranks
}
\description{
-Summarise the results of \code{\link{diffSCESets}}. Calculates the Median
+Summarise the results of \code{\link{diffSCEs}}. Calculates the Median
Absolute Deviation (MAD), Mean Absolute Error (MAE) and Root Mean Squared
Error (RMSE) for the various properties and ranks them.
}
\examples{
sim1 <- splatSimulate(nGenes = 1000, batchCells = 20)
sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
-difference <- diffSCESets(list(Splat = sim1, Simple = sim2), ref = "Simple")
+difference <- diffSCEs(list(Splat = sim1, Simple = sim2), ref = "Simple")
summary <- summariseDiff(difference)
head(summary)
}
--
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