From f1e2e070fe70780fdb94058eeb538ec23088d8fd Mon Sep 17 00:00:00 2001
From: Luke Zappia <lazappi@users.noreply.github.com>
Date: Tue, 30 Jan 2018 14:52:23 +1100
Subject: [PATCH] Fix other QC names
---
R/compare.R | 26 ++++++++++++++------------
1 file changed, 14 insertions(+), 12 deletions(-)
diff --git a/R/compare.R b/R/compare.R
index 27ca039..91b78a7 100644
--- a/R/compare.R
+++ b/R/compare.R
@@ -79,8 +79,8 @@ compareSCEs <- function(sces, point.size = 0.1, point.alpha = 0.1,
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))
+ n.features <- colData(sce)$total_features_by_counts
+ colData(sce)$PctZero <- 100 * (1 - n.features / nrow(sce))
sces[[name]] <- sce
}
@@ -140,7 +140,7 @@ compareSCEs <- function(sces, point.size = 0.1, point.alpha = 0.1,
theme_minimal()
z.gene <- ggplot(features,
- aes_string(x = "Dataset", y = "pct_dropout_counts",
+ aes_string(x = "Dataset", y = "pct_dropout_by_counts",
colour = "Dataset")) +
geom_boxplot() +
scale_y_continuous(limits = c(0, 100)) +
@@ -160,7 +160,8 @@ compareSCEs <- function(sces, point.size = 0.1, point.alpha = 0.1,
theme_minimal()
mean.zeros <- ggplot(features,
- aes_string(x = "MeanCounts", y = "pct_dropout_counts",
+ aes_string(x = "MeanCounts",
+ y = "pct_dropout_by_counts",
colour = "Dataset", fill = "Dataset")) +
geom_point(size = point.size, alpha = point.alpha) +
scale_x_log10(labels = scales::comma) +
@@ -304,8 +305,8 @@ diffSCEs <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
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))
+ n.features <- colData(sce)$total_features_by_counts
+ colData(sce)$PctZero <- 100 * (1 - n.features / nrow(sce))
rowData(sce)$RankCounts <- rank(rowData(sce)$mean_counts)
sces[[name]] <- sce
}
@@ -315,12 +316,12 @@ 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_counts)
+ ref.z.gene <- sort(rowData(ref.sce)$pct_dropout_by_counts)
ref.z.cell <- sort(colData(ref.sce)$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)$pct_dropout_counts[ref.rank.ord]
+ ref.z.gene.rank <- rowData(ref.sce)$pct_dropout_by_counts[ref.rank.ord]
for (name in names(sces)) {
sce <- sces[[name]]
@@ -335,8 +336,8 @@ diffSCEs <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
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)$pct_dropout_counts -
+ rowData(sce)$pct_dropout_by_counts)]
+ rowData(sce)$RankDiffZeros <- rowData(sce)$pct_dropout_by_counts -
rowData(sce)$RefRankZeros
colData(sce)$RefRankZeros <- ref.z.cell[rank(
colData(sce)$PctZero)]
@@ -345,7 +346,7 @@ diffSCEs <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
rowData(sce)$MeanRankVarDiff <- rowData(sce)$VarLogCPM -
ref.vars.rank[rowData(sce)$RankCounts]
- rowData(sce)$MeanRankZerosDiff <- rowData(sce)$pct_dropout_counts -
+ rowData(sce)$MeanRankZerosDiff <- rowData(sce)$pct_dropout_by_counts -
ref.z.gene.rank[rowData(sce)$RankCounts]
sces[[name]] <- sce
@@ -480,7 +481,8 @@ diffSCEs <- function(sces, ref, point.size = 0.1, point.alpha = 0.1,
theme_minimal()
z.gene.qq <- ggplot(features,
- aes_string(x = "RefRankZeros", y = "pct_dropout_counts",
+ aes_string(x = "RefRankZeros",
+ y = "pct_dropout_by_counts",
colour = "Dataset")) +
geom_abline(intercept = 0, slope = 1, colour = "red") +
geom_point(size = point.size, alpha = point.alpha) +
--
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