diff --git a/DESCRIPTION b/DESCRIPTION
index 554bc87e0ae012089ff89f510c7eafd8af824ef0..0db4d2baa9c6a3cd125ff05e7b9fd6c9ce5c4f59 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,8 +1,8 @@
Package: splatter
Type: Package
Title: Simple Simulation of Single-cell RNA Sequencing Data
-Version: 1.9.2.9003
-Date: 2019-07-17
+Version: 1.9.3.9004
+Date: 2019-08-08
Author: Luke Zappia
Authors@R:
c(person("Luke", "Zappia", role = c("aut", "cre"),
@@ -37,7 +37,7 @@ Imports:
matrixStats,
methods,
scales,
- scater (>= 1.7.4),
+ scater (>= 1.13.11),
stats,
SummarizedExperiment,
utils,
diff --git a/NEWS.md b/NEWS.md
index f3095ecd182dbb4f8ca0d30fe522a91b3c09eb93..22109a01d816de25b9654a86859ca92513a598b2 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,11 @@
+### Version 1.9.3.9004 (2019-08-08)
+
+* Merge master into splotch branch
+
+## Version 1.9.3 (2019-08-08)
+
+* Remove deprecated scater functions
+
### Version 1.9.2.9003 (2019-07-17)
* Topologically sort paths
diff --git a/R/compare.R b/R/compare.R
index 8e8b20b795aec9b5e02f1fc8e8f18506f0adecf9..07bd63b36ee5861b72e4912c4676a7cc261a18c5 100644
--- a/R/compare.R
+++ b/R/compare.R
@@ -79,13 +79,15 @@ compareSCEs <- function(sces, point.size = 0.1, point.alpha = 0.1,
sce <- sces[[name]]
rowData(sce)$Dataset <- name
colData(sce)$Dataset <- name
- sce <- scater::calculateQCMetrics(sce)
- cpm(sce) <- scater::calculateCPM(sce, use_size_factors = FALSE)
+ sce <- scater::addQCPerCell(sce)
+ sce <- scater::addQCPerFeature(sce)
+ cpm(sce) <- scater::calculateCPM(sce)
sce <- addFeatureStats(sce, "counts")
sce <- addFeatureStats(sce, "cpm")
sce <- addFeatureStats(sce, "cpm", log = TRUE)
- n.features <- colData(sce)$total_features_by_counts
+ n.features <- colData(sce)$detected
colData(sce)$PctZero <- 100 * (1 - n.features / nrow(sce))
+ rowData(sce)$PctZero <- 100 - rowData(sce)$detected
var.genes <- rev(order(rowData(sce)$VarLogCPM))[1:100]
var.cpm <- log2(cpm(sce)[var.genes, ] + 1)
@@ -150,7 +152,7 @@ compareSCEs <- function(sces, point.size = 0.1, point.alpha = 0.1,
theme_minimal()
libs <- ggplot(cells,
- aes_string(x = "Dataset", y = "total_counts",
+ aes_string(x = "Dataset", y = "sum",
colour = "Dataset")) +
geom_boxplot() +
scale_y_continuous(labels = scales::comma) +
@@ -160,7 +162,7 @@ compareSCEs <- function(sces, point.size = 0.1, point.alpha = 0.1,
theme_minimal()
z.gene <- ggplot(features,
- aes_string(x = "Dataset", y = "pct_dropout_by_counts",
+ aes_string(x = "Dataset", y = "PctZero",
colour = "Dataset")) +
geom_boxplot() +
scale_y_continuous(limits = c(0, 100)) +
@@ -180,8 +182,8 @@ compareSCEs <- function(sces, point.size = 0.1, point.alpha = 0.1,
theme_minimal()
mean.zeros <- ggplot(features,
- aes_string(x = "MeanCounts",
- y = "pct_dropout_by_counts",
+ aes_string(x = "mean",
+ y = "PctZero",
colour = "Dataset", fill = "Dataset")) +
geom_point(size = point.size, alpha = point.alpha) +
scale_x_log10(labels = scales::comma) +
@@ -336,13 +338,15 @@ diffSCEs <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
}
rowData(sce)$Dataset <- name
colData(sce)$Dataset <- name
- sce <- scater::calculateQCMetrics(sce)
- cpm(sce) <- scater::calculateCPM(sce, use_size_factors = FALSE)
+ sce <- scater::addQCPerCell(sce)
+ sce <- scater::addQCPerFeature(sce)
+ cpm(sce) <- scater::calculateCPM(sce)
sce <- addFeatureStats(sce, "counts")
sce <- addFeatureStats(sce, "cpm", log = TRUE)
- n.features <- colData(sce)$total_features_by_counts
+ n.features <- colData(sce)$detected
colData(sce)$PctZero <- 100 * (1 - n.features / nrow(sce))
- rowData(sce)$RankCounts <- rank(rowData(sce)$mean_counts)
+ rowData(sce)$RankCounts <- rank(rowData(sce)$mean)
+ rowData(sce)$PctZero <- 100 - rowData(sce)$detected
sces[[name]] <- sce
}
@@ -350,13 +354,13 @@ diffSCEs <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
ref.means <- sort(rowData(ref.sce)$MeanLogCPM)
ref.vars <- sort(rowData(ref.sce)$VarLogCPM)
- ref.libs <- sort(colData(ref.sce)$total_counts)
- ref.z.gene <- sort(rowData(ref.sce)$pct_dropout_by_counts)
+ ref.libs <- sort(colData(ref.sce)$sum)
+ ref.z.gene <- sort(rowData(ref.sce)$PctZero)
ref.z.cell <- sort(colData(ref.sce)$PctZero)
ref.rank.ord <- order(rowData(ref.sce)$RankCounts)
ref.vars.rank <- rowData(ref.sce)$VarLogCPM[ref.rank.ord]
- ref.z.gene.rank <- rowData(ref.sce)$pct_dropout_by_counts[ref.rank.ord]
+ ref.z.gene.rank <- rowData(ref.sce)$PctZero[ref.rank.ord]
for (name in names(sces)) {
sce <- sces[[name]]
@@ -367,12 +371,11 @@ diffSCEs <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
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 <- ref.libs[rank(colData(sce)$sum)]
+ colData(sce)$RankDiffLibSize <- colData(sce)$sum -
colData(sce)$RefRankLibSize
- rowData(sce)$RefRankZeros <- ref.z.gene[rank(
- rowData(sce)$pct_dropout_by_counts)]
- rowData(sce)$RankDiffZeros <- rowData(sce)$pct_dropout_by_counts -
+ rowData(sce)$RefRankZeros <- ref.z.gene[rank(rowData(sce)$PctZero)]
+ rowData(sce)$RankDiffZeros <- rowData(sce)$PctZero -
rowData(sce)$RefRankZeros
colData(sce)$RefRankZeros <- ref.z.cell[rank(
colData(sce)$PctZero)]
@@ -381,7 +384,7 @@ diffSCEs <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
rowData(sce)$MeanRankVarDiff <- rowData(sce)$VarLogCPM -
ref.vars.rank[rowData(sce)$RankCounts]
- rowData(sce)$MeanRankZerosDiff <- rowData(sce)$pct_dropout_by_counts -
+ rowData(sce)$MeanRankZerosDiff <- rowData(sce)$PctZero -
ref.z.gene.rank[rowData(sce)$RankCounts]
sces[[name]] <- sce
@@ -517,7 +520,7 @@ diffSCEs <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
z.gene.qq <- ggplot(features,
aes_string(x = "RefRankZeros",
- y = "pct_dropout_by_counts",
+ y = "PctZero",
colour = "Dataset")) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
geom_point(size = point.size, alpha = point.alpha) +
@@ -871,7 +874,7 @@ summariseDiff <- function(diff) {
row.ks.stats <- c(Mean = "MeanLogCPM",
Variance = "VarLogCPM",
- ZerosGene = "pct_dropout_by_counts",
+ ZerosGene = "PctZero",
MeanVar = NA,
MeanZeros = NA)
@@ -892,7 +895,7 @@ summariseDiff <- function(diff) {
col.stats <- c(LibSize = "RankDiffLibSize",
ZerosCell = "RankDiffZeros")
- col.ks.stats <- c(LibSize = "total_counts",
+ col.ks.stats <- c(LibSize = "sum",
ZerosCell = "PctZero")
col.mad <- summariseStats(diff$ColData, "Dataset", col.stats, "MAD")
diff --git a/vignettes/splatter.Rmd b/vignettes/splatter.Rmd
index f73d3a97e072dda86eba72686945538817db7bdf..34a4138efe5988d3a96c1b59f78bd6243703f878 100644
--- a/vignettes/splatter.Rmd
+++ b/vignettes/splatter.Rmd
@@ -433,8 +433,7 @@ number of expressed genes against the library size:
```{r comparison-libsize-features}
library("ggplot2")
-ggplot(comparison$ColData,
- aes(x = total_counts, y = total_features_by_counts, colour = Dataset)) +
+ggplot(comparison$ColData, aes(x = sum, y = detected, colour = Dataset)) +
geom_point()
```