Update addFeatureStats col names

Make it clear which are added by Splatter

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)

R/compare.R

@@ -80,7 +80,7 @@ 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_boxplot() +
@@ -89,7 +89,7 @@ compareSCESets <- function(sces) {
         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_boxplot() +
@@ -99,7 +99,7 @@ compareSCESets <- function(sces) {
         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() +
@@ -136,7 +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() +
@@ -256,22 +256,23 @@ diffSCESets <- function(sces, ref) {
 
     ref.sce <- sces[[ref]]
 
-    ref.means <- sort(fData(ref.sce)$mean_log_cpm)
-    ref.vars <- sort(fData(ref.sce)$var_log_cpm)
+    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.vars.meanrank <- fData(ref.sce)$var_log_cpm[order(fData(ref.sce)$exprs_rank)]
-    ref.z.gene.meanrank <- fData(ref.sce)$pct_dropout[order(fData(ref.sce)$exprs_rank)]
+    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)$RefRankMeanLogCPM <- ref.means[rank(fData(sce)$mean_log_cpm)]
-        fData(sce)$RankDiffMeanLogCPM <- fData(sce)$mean_log_cpm -
+        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)$var_log_cpm)]
-        fData(sce)$RankDiffVarLogCPM <- fData(sce)$var_log_cpm -
+        fData(sce)$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 -
@@ -283,10 +284,10 @@ diffSCESets <- function(sces, ref) {
         pData(sce)$RankDiffZeros <- pData(sce)$pct_dropout -
             pData(sce)$RefRankZeros
 
-        fData(sce)$MeanRankVarDiff <- fData(sce)$var_log_cpm -
-            ref.vars.meanrank[fData(sce)$exprs_rank]
+        fData(sce)$MeanRankVarDiff <- fData(sce)$VarLogCPM -
+            ref.vars.rank[fData(sce)$exprs_rank]
         fData(sce)$MeanRankZerosDiff <- fData(sce)$pct_dropout -
-            ref.z.gene.meanrank[fData(sce)$exprs_rank]
+            ref.z.gene.rank[fData(sce)$exprs_rank]
 
         sces[[name]] <- sce
     }
@@ -376,7 +377,7 @@ diffSCESets <- function(sces, ref) {
         theme_minimal()
 
     means.qq <- ggplot(fData.all,
-                       aes_string(x = "RefRankMeanLogCPM", y = "mean_log_cpm",
+                       aes_string(x = "RefRankMeanLogCPM", y = "MeanLogCPM",
                                   colour = "Dataset")) +
         geom_abline(intercept = 0, slope = 1, colour = "red") +
         geom_point() +
@@ -386,7 +387,7 @@ diffSCESets <- function(sces, ref) {
         theme_minimal()
 
     vars.qq <- ggplot(fData.all,
-                      aes_string(x = "RefRankVarLogCPM", y = "var_log_cpm",
+                      aes_string(x = "RefRankVarLogCPM", y = "VarLogCPM",
                                  colour = "Dataset")) +
         geom_abline(intercept = 0, slope = 1, colour = "red") +
         geom_point() +

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}.
 }