From 4ff20b7440e2c81915e9551ae8fa341f8d1e49d3 Mon Sep 17 00:00:00 2001
From: Luke Zappia <lazappi@users.noreply.github.com>
Date: Mon, 15 Apr 2019 15:28:58 +1000
Subject: [PATCH] Minor changes to documentation
Fixes #64
Also remove lingering references to phenoData and featureData
---
R/AllClasses.R | 2 +-
R/compare.R | 89 ++++++++++++++++++-----------------
R/splat-simulate.R | 6 +--
man/SplatParams.Rd | 2 +-
man/compareSCEs.Rd | 4 +-
man/diffSCEs.Rd | 4 +-
man/splatSimulate.Rd | 6 +--
tests/testthat/test-compare.R | 12 ++---
vignettes/splatter.Rmd | 12 ++---
9 files changed, 71 insertions(+), 66 deletions(-)
diff --git a/R/AllClasses.R b/R/AllClasses.R
index dff138a..eb6a414 100644
--- a/R/AllClasses.R
+++ b/R/AllClasses.R
@@ -126,7 +126,7 @@ setClass("SimpleParams",
#' \describe{
#' \item{\code{[de.prob]}}{Probability that a gene is differentially
#' expressed in a group. Can be a vector.}
-#' \item{\code{[de.loProb]}}{Probability that a differentially
+#' \item{\code{[de.downProb]}}{Probability that a differentially
#' expressed gene is down-regulated. Can be a vector.}
#' \item{\code{[de.facLoc]}}{Location (meanlog) parameter for the
#' differential expression factor log-normal distribution. Can be a
diff --git a/R/compare.R b/R/compare.R
index 3743e64..88fede5 100644
--- a/R/compare.R
+++ b/R/compare.R
@@ -14,9 +14,9 @@
#' The returned list has three items:
#'
#' \describe{
-#' \item{\code{FeatureData}}{Combined feature data from the provided
+#' \item{\code{RowData}}{Combined row data from the provided
#' SingleCellExperiments.}
-#' \item{\code{PhenoData}}{Combined pheno data from the provided
+#' \item{\code{ColData}}{Combined column data from the provided
#' SingleCellExperiments.}
#' \item{\code{Plots}}{Comparison plots
#' \describe{
@@ -177,8 +177,8 @@ compareSCEs <- function(sces, point.size = 0.1, point.alpha = 0.1,
mean.zeros <- mean.zeros + geom_smooth()
}
- comparison <- list(FeatureData = features,
- PhenoData = cells,
+ comparison <- list(RowData = features,
+ ColData = cells,
Plots = list(Means = means,
Variances = vars,
MeanVar = mean.var,
@@ -217,9 +217,9 @@ compareSCEs <- function(sces, point.size = 0.1, point.alpha = 0.1,
#'
#' \describe{
#' \item{\code{Reference}}{The SingleCellExperiment used as the reference.}
-#' \item{\code{FeatureData}}{Combined feature data from the provided
+#' \item{\code{RowData}}{Combined feature data from the provided
#' SingleCellExperiments.}
-#' \item{\code{PhenoData}}{Combined pheno data from the provided
+#' \item{\code{ColData}}{Combined column data from the provided
#' SingleCellExperiments.}
#' \item{\code{Plots}}{Difference plots
#' \describe{
@@ -509,8 +509,8 @@ diffSCEs <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
}
comparison <- list(Reference = ref.sce,
- FeatureData = features,
- PhenoData = cells,
+ RowData = features,
+ ColData = cells,
Plots = list(Means = means,
Variances = vars,
MeanVar = mean.var,
@@ -824,10 +824,10 @@ makeOverallPanel <- function(comp, diff, title = "Overall comparison",
#' @export
summariseDiff <- function(diff) {
- datasets <- unique(diff$PhenoData$Dataset)
+ datasets <- unique(diff$ColData$Dataset)
- fData.mads <- sapply(datasets, function(dataset) {
- df <- diff$FeatureData[diff$FeatureData$Dataset == dataset, ]
+ rowData.mads <- sapply(datasets, function(dataset) {
+ df <- diff$RowData[diff$RowData$Dataset == dataset, ]
mean <- median(abs(df$RankDiffMeanLogCPM))
var <- median(abs(df$RankDiffVarLogCPM))
zeros <- median(abs(df$RankDiffZeros))
@@ -836,27 +836,29 @@ summariseDiff <- function(diff) {
return(c(Mean = mean, Variance = var, ZerosGene = zeros,
MeanVar = mean.var, MeanZeros = mean.zeros))
})
- fData.mads.z <- t(scale(t(fData.mads)))
+ rowData.mads.z <- t(scale(t(rowData.mads)))
- pData.mads <- sapply(datasets, function(dataset) {
- df <- diff$PhenoData[diff$PhenoData$Dataset == dataset, ]
+ colData.mads <- sapply(datasets, function(dataset) {
+ df <- diff$ColData[diff$ColData$Dataset == dataset, ]
lib.size <- median(abs(df$RankDiffLibSize))
zeros <- median(abs(df$RankDiffZeros))
return(c(LibSize = lib.size, ZerosCell = zeros))
})
- pData.mads.z <- t(scale(t(pData.mads)))
+ colData.mads.z <- t(scale(t(colData.mads)))
- mads <- data.frame(Dataset = datasets, t(fData.mads), t(pData.mads))
- mads.z <- data.frame(Dataset = datasets, t(fData.mads.z), t(pData.mads.z))
+ mads <- data.frame(Dataset = datasets, t(rowData.mads), t(colData.mads))
+ mads.z <- data.frame(Dataset = datasets, t(rowData.mads.z),
+ t(colData.mads.z))
- fData.ranks <- matrixStats::rowRanks(fData.mads)
- pData.ranks <- matrixStats::rowRanks(pData.mads)
+ rowData.ranks <- matrixStats::rowRanks(rowData.mads)
+ colData.ranks <- matrixStats::rowRanks(colData.mads)
- ranks.mads <- data.frame(Dataset = datasets, t(fData.ranks), t(pData.ranks))
+ ranks.mads <- data.frame(Dataset = datasets, t(rowData.ranks),
+ t(colData.ranks))
colnames(ranks.mads) <- paste0(colnames(mads), "Rank")
- fData.maes <- sapply(datasets, function(dataset) {
- df <- diff$FeatureData[diff$FeatureData$Dataset == dataset, ]
+ rowData.maes <- sapply(datasets, function(dataset) {
+ df <- diff$RowData[diff$RowData$Dataset == dataset, ]
mean <- mean(abs(df$RankDiffMeanLogCPM))
var <- mean(abs(df$RankDiffVarLogCPM))
zeros <- mean(abs(df$RankDiffZeros))
@@ -865,27 +867,28 @@ summariseDiff <- function(diff) {
return(c(Mean = mean, Variance = var, ZerosGene = zeros,
MeanVar = mean.var, MeanZeros = mean.zeros))
})
- fData.maes.z <- t(scale(t(fData.maes)))
+ rowData.maes.z <- t(scale(t(rowData.maes)))
- pData.maes <- sapply(datasets, function(dataset) {
- df <- diff$PhenoData[diff$PhenoData$Dataset == dataset, ]
+ colData.maes <- sapply(datasets, function(dataset) {
+ df <- diff$ColData[diff$ColData$Dataset == dataset, ]
lib.size <- mean(abs(df$RankDiffLibSize))
zeros <- mean(abs(df$RankDiffZeros))
return(c(LibSize = lib.size, ZerosCell = zeros))
})
- pData.maes.z <- t(scale(t(pData.maes)))
+ colData.maes.z <- t(scale(t(colData.maes)))
- maes <- data.frame(Dataset = datasets, t(fData.maes), t(pData.maes))
- maes.z <- data.frame(Dataset = datasets, t(fData.maes.z), t(pData.maes.z))
+ maes <- data.frame(Dataset = datasets, t(rowData.maes), t(colData.maes))
+ maes.z <- data.frame(Dataset = datasets, t(rowData.maes.z),
+ t(colData.maes.z))
- fData.ranks <- matrixStats::rowRanks(fData.maes)
- pData.ranks <- matrixStats::rowRanks(pData.maes)
+ rowData.ranks <- matrixStats::rowRanks(rowData.maes)
+ colData.ranks <- matrixStats::rowRanks(colData.maes)
- ranks.maes <- data.frame(Dataset = datasets, t(fData.ranks), t(pData.ranks))
+ ranks.maes <- data.frame(Dataset = datasets, t(rowData.ranks), t(colData.ranks))
colnames(ranks.maes) <- paste0(colnames(mads), "Rank")
- fData.rmse <- sapply(datasets, function(dataset) {
- df <- diff$FeatureData[diff$FeatureData$Dataset == dataset, ]
+ rowData.rmse <- sapply(datasets, function(dataset) {
+ df <- diff$RowData[diff$RowData$Dataset == dataset, ]
mean <- sqrt(mean(df$RankDiffMeanLogCPM ^ 2))
var <- sqrt(mean(df$RankDiffVarLogCPM ^ 2))
zeros <- sqrt(mean(df$RankDiffZeros ^ 2))
@@ -894,23 +897,25 @@ summariseDiff <- function(diff) {
return(c(Mean = mean, Variance = var, ZerosGene = zeros,
MeanVar = mean.var, MeanZeros = mean.zeros))
})
- fData.rmse.z <- t(scale(t(fData.rmse)))
+ rowData.rmse.z <- t(scale(t(rowData.rmse)))
- pData.rmse <- sapply(datasets, function(dataset) {
- df <- diff$PhenoData[diff$PhenoData$Dataset == dataset, ]
+ colData.rmse <- sapply(datasets, function(dataset) {
+ df <- diff$ColData[diff$ColData$Dataset == dataset, ]
lib.size <- sqrt(mean(df$RankDiffLibSize ^ 2))
zeros <- sqrt(mean(df$RankDiffZeros ^ 2))
return(c(LibSize = lib.size, ZerosCell = zeros))
})
- pData.rmse.z <- t(scale(t(pData.rmse)))
+ colData.rmse.z <- t(scale(t(colData.rmse)))
- rmse <- data.frame(Dataset = datasets, t(fData.rmse), t(pData.rmse))
- rmse.z <- data.frame(Dataset = datasets, t(fData.rmse.z), t(pData.rmse.z))
+ rmse <- data.frame(Dataset = datasets, t(rowData.rmse), t(colData.rmse))
+ rmse.z <- data.frame(Dataset = datasets, t(rowData.rmse.z),
+ t(colData.rmse.z))
- fData.ranks <- matrixStats::rowRanks(fData.rmse)
- pData.ranks <- matrixStats::rowRanks(pData.rmse)
+ rowData.ranks <- matrixStats::rowRanks(rowData.rmse)
+ colData.ranks <- matrixStats::rowRanks(colData.rmse)
- ranks.rmse <- data.frame(Dataset = datasets, t(fData.ranks), t(pData.ranks))
+ ranks.rmse <- data.frame(Dataset = datasets, t(rowData.ranks),
+ t(colData.ranks))
colnames(ranks.rmse) <- paste0(colnames(rmse), "Rank")
mads <- stats::reshape(mads, varying = 2:8, direction = "long",
diff --git a/R/splat-simulate.R b/R/splat-simulate.R
index fbf9ff1..6c9a0e7 100644
--- a/R/splat-simulate.R
+++ b/R/splat-simulate.R
@@ -42,7 +42,7 @@
#' \code{\link{assays}} (for gene by cell matrices) slots. This additional
#' information includes:
#' \describe{
-#' \item{\code{phenoData}}{
+#' \item{\code{colData}}{
#' \describe{
#' \item{Cell}{Unique cell identifier.}
#' \item{Group}{The group or path the cell belongs to.}
@@ -50,7 +50,7 @@
#' \item{Step (paths only)}{how far along the path each cell is.}
#' }
#' }
-#' \item{\code{featureData}}{
+#' \item{\code{rowData}}{
#' \describe{
#' \item{Gene}{Unique gene identifier.}
#' \item{BaseGeneMean}{The base expression level for that gene.}
@@ -66,7 +66,7 @@
#' non-linear changes in expression along a path.}
#' }
#' }
-#' \item{\code{assayData}}{
+#' \item{\code{assays}}{
#' \describe{
#' \item{BatchCellMeans}{The mean expression of genes in each cell
#' after adding batch effects.}
diff --git a/man/SplatParams.Rd b/man/SplatParams.Rd
index fbd581d..23b089c 100644
--- a/man/SplatParams.Rd
+++ b/man/SplatParams.Rd
@@ -70,7 +70,7 @@ The Splatter simulation requires the following parameters:
\describe{
\item{\code{[de.prob]}}{Probability that a gene is differentially
expressed in a group. Can be a vector.}
- \item{\code{[de.loProb]}}{Probability that a differentially
+ \item{\code{[de.downProb]}}{Probability that a differentially
expressed gene is down-regulated. Can be a vector.}
\item{\code{[de.facLoc]}}{Location (meanlog) parameter for the
differential expression factor log-normal distribution. Can be a
diff --git a/man/compareSCEs.Rd b/man/compareSCEs.Rd
index 711a2be..f0408d0 100644
--- a/man/compareSCEs.Rd
+++ b/man/compareSCEs.Rd
@@ -30,9 +30,9 @@ basic plots comparing them.
The returned list has three items:
\describe{
- \item{\code{FeatureData}}{Combined feature data from the provided
+ \item{\code{RowData}}{Combined row data from the provided
SingleCellExperiments.}
- \item{\code{PhenoData}}{Combined pheno data from the provided
+ \item{\code{ColData}}{Combined column data from the provided
SingleCellExperiments.}
\item{\code{Plots}}{Comparison plots
\describe{
diff --git a/man/diffSCEs.Rd b/man/diffSCEs.Rd
index a24a5c2..3e75cb6 100644
--- a/man/diffSCEs.Rd
+++ b/man/diffSCEs.Rd
@@ -41,9 +41,9 @@ The returned list has five items:
\describe{
\item{\code{Reference}}{The SingleCellExperiment used as the reference.}
- \item{\code{FeatureData}}{Combined feature data from the provided
+ \item{\code{RowData}}{Combined feature data from the provided
SingleCellExperiments.}
- \item{\code{PhenoData}}{Combined pheno data from the provided
+ \item{\code{ColData}}{Combined column data from the provided
SingleCellExperiments.}
\item{\code{Plots}}{Difference plots
\describe{
diff --git a/man/splatSimulate.Rd b/man/splatSimulate.Rd
index 5385187..578477f 100644
--- a/man/splatSimulate.Rd
+++ b/man/splatSimulate.Rd
@@ -67,7 +67,7 @@ information), \code{\link{rowData}} (for gene specific information) or
\code{\link{assays}} (for gene by cell matrices) slots. This additional
information includes:
\describe{
- \item{\code{phenoData}}{
+ \item{\code{colData}}{
\describe{
\item{Cell}{Unique cell identifier.}
\item{Group}{The group or path the cell belongs to.}
@@ -75,7 +75,7 @@ information includes:
\item{Step (paths only)}{how far along the path each cell is.}
}
}
- \item{\code{featureData}}{
+ \item{\code{rowData}}{
\describe{
\item{Gene}{Unique gene identifier.}
\item{BaseGeneMean}{The base expression level for that gene.}
@@ -91,7 +91,7 @@ information includes:
non-linear changes in expression along a path.}
}
}
- \item{\code{assayData}}{
+ \item{\code{assays}}{
\describe{
\item{BatchCellMeans}{The mean expression of genes in each cell
after adding batch effects.}
diff --git a/tests/testthat/test-compare.R b/tests/testthat/test-compare.R
index 04d6520..29540df 100644
--- a/tests/testthat/test-compare.R
+++ b/tests/testthat/test-compare.R
@@ -8,10 +8,10 @@ difference <- diffSCEs(list(Splat = sim1, Simple = sim2), ref = "Simple")
test_that("compareSCEs works", {
expect_length(comparison, 3)
- expect_true(all(c("FeatureData", "PhenoData", "Plots") %in%
+ expect_true(all(c("RowData", "ColData", "Plots") %in%
names(comparison)))
- checkmate::expect_class(comparison$PhenoData, "data.frame")
- checkmate::expect_class(comparison$FeatureData, "data.frame")
+ checkmate::expect_class(comparison$ColData, "data.frame")
+ checkmate::expect_class(comparison$RowData, "data.frame")
expect_length(comparison$Plots, 7)
expect_true(all(c("Means", "Variances", "MeanVar", "LibrarySizes",
"ZerosGene", "ZerosCell", "MeanZeros") %in%
@@ -23,11 +23,11 @@ test_that("compareSCEs works", {
test_that("diffSCEs works", {
expect_length(difference, 5)
- expect_true(all(c("Reference", "FeatureData", "PhenoData", "Plots",
+ expect_true(all(c("Reference", "RowData", "ColData", "Plots",
"QQPlots") %in% names(difference)))
checkmate::expect_class(difference$Reference, "SingleCellExperiment")
- checkmate::expect_class(difference$PhenoData, "data.frame")
- checkmate::expect_class(difference$FeatureData, "data.frame")
+ checkmate::expect_class(difference$ColData, "data.frame")
+ checkmate::expect_class(difference$RowData, "data.frame")
expect_length(difference$Plots, 7)
expect_true(all(c("Means", "Variances", "MeanVar", "LibrarySizes",
"ZerosGene", "ZerosCell", "MeanZeros") %in%
diff --git a/vignettes/splatter.Rmd b/vignettes/splatter.Rmd
index af3eeff..be82bd3 100644
--- a/vignettes/splatter.Rmd
+++ b/vignettes/splatter.Rmd
@@ -121,7 +121,7 @@ The parameters required for the Splat simulation are briefly described here:
* **Differential expression parameters**
* `de.prob` - Probability that a gene is differentially expressed in each
group or path.
- * `de.loProb` - Probability that a differentially expressed gene is
+ * `de.downProb` - Probability that a differentially expressed gene is
down-regulated.
* `de.facLoc` - Location (meanlog) parameter for the differential expression
factor log-normal distribution.
@@ -298,12 +298,12 @@ For more details about the `SingleCellExperiment` object refer to the [vignette]
The `splatSimulate` function outputs the following additional information about
the simulation:
-* **Cell information (`pData`)**
+* **Cell information (`colData`)**
* `Cell` - Unique cell identifier.
* `Group` - The group or path the cell belongs to.
* `ExpLibSize` - The expected library size for that cell.
* `Step` (paths only) - How far along the path each cell is.
-* **Gene information (`fData`)**
+* **Gene information (`rowData`)**
* `Gene` - Unique gene identifier.
* `BaseGeneMean` - The base expression level for that gene.
* `OutlierFactor` - Expression outlier factor for that gene (1 is not an
@@ -313,7 +313,7 @@ the simulation:
in a particular group (1 is not differentially expressed).
* `GeneMean[Group]` - Expression level of a gene in a particular group after
applying differential expression factors.
-* **Gene by cell information (`assayData`)**
+* **Gene by cell information (`assays`)**
* `BaseCellMeans` - The expression of genes in each cell adjusted for
expected library size.
* `BCV` - The Biological Coefficient of Variation for each gene in
@@ -489,7 +489,7 @@ names(comparison$Plots)
```
The returned list has three items. The first two are the combined datasets by
-gene (`FeatureData`) and by cell (`PhenoData`) and the third contains some
+gene (`RowData`) and by cell (`ColData`) and the third contains some
comparison plots (produced using `ggplot2`), for example a plot of the
distribution of means:
@@ -503,7 +503,7 @@ number of expressed genes against the library size:
```{r comparison-libsize-features}
library("ggplot2")
-ggplot(comparison$PhenoData,
+ggplot(comparison$ColData,
aes(x = total_counts, y = total_features_by_counts, colour = Dataset)) +
geom_point()
```
--
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