Update plots

NAMESPACE

@@ -164,6 +164,8 @@ importFrom(ggplot2,geom_point)
 importFrom(ggplot2,geom_segment)
 importFrom(ggplot2,geom_step)
 importFrom(ggplot2,geom_vline)
+importFrom(ggplot2,geom_line)
+importFrom(ggplot2,geom_text)
 importFrom(ggplot2,ggplot)
 importFrom(ggplot2,ggtitle)
 importFrom(ggplot2,guide_legend)

R/CorrelationMethods.R

@@ -486,10 +486,10 @@ pairs.DataFrame <- function (x, labels, panel = points, ..., horInd = 1:nc, verI
                              , tagCountThreshold, applyThresholdBoth
                              , digits) {
   # Select samples
-  if (all(samples %in% colnames(expr.table))) {
-    expr.table <- expr.table[,samples]
-  } else if(samples == "all"){
+  if(samples == "all"){
     samples <- colnames(expr.table)
+  } else if (all(samples %in% colnames(expr.table))) {
+    expr.table <- expr.table[,samples]
   } else stop("'samples' parameter must be either \"all\" or a character vector of valid sample labels!")
   nr.samples <- length(samples)
 
@@ -541,15 +541,15 @@ pairs.DataFrame <- function (x, labels, panel = points, ..., horInd = 1:nc, verI
   }
 
   pairs( expr.table
-       , lower.panel = pointsUnique
-       , upper.panel = panel.cor
-       , pch = "."
-       , cex = 4
-       , log = "xy"
-       , las = 1
-       , xaxp = c(1,10,1)
-       , yaxp = c(1,10,1)
-       , labels = samples)
+      , lower.panel = pointsUnique
+      , upper.panel = panel.cor
+      , pch = "."
+      , cex = 4
+      , log = "xy"
+      , las = 1
+      , xaxp = c(1,10,1)
+      , yaxp = c(1,10,1)
+      , labels = samples)
 
   # Return a correlation matrix
   corr.m <- matrix(1, nr.samples, nr.samples)

R/ExportMethods.R

@@ -64,9 +64,7 @@ NULL
 #'   ggplot2::ggtitle("prim6 replicates")
 #' tagClustersGR(exampleCAGEexp) |> plotReverseCumulatives()
 #' 
-#' @importFrom ggplot2 aes geom_abline geom_step geom_vline ggplot ggtitle
-#' @importFrom ggplot2 guides guide_legend labs
-#' @importFrom ggplot2 scale_color_manual scale_x_log10 scale_y_log10
+#' @importFrom ggplot2 aes geom_line geom_abline facet_wrap geom_text geom_vline ggplot scale_x_log10 scale_y_log10
 #' @importFrom rlang .data
 #' @importFrom scales hue_pal
 #' @importFrom stats cor median
@@ -101,10 +99,6 @@ setGeneric( "plotReverseCumulatives",
     group <- "group"
   }
 
-  if (is.null(object@metadata$colData$Colors)) {
-    object@metadata$colData$Colors <- scales::hue_pal()(length(object))
-  }
-
   toCumSums <- function(exprValues, sampleName) {
     # See benchmarks/sorted-abundance-benchmark.Rmd in the CAGEr's Git repository
     exprValues <- exprValues[exprValues != 0]
@@ -122,14 +116,9 @@ setGeneric( "plotReverseCumulatives",
   # Enforce original sample order
   DF$plotOrder <- DF$sampleLabels |> ordered(unique(DF$sampleLabels))
 
-  p <- as.data.frame(DF) |>
+  intermediate_df <- as.data.frame(DF) |>
       merge( object@metadata$colData[,c('sampleLabels', group)] |> as.data.frame()
-           , by="sampleLabels") |> ggplot() +
-    aes(.data$x, .data$y, col = .data$plotOrder) +
-    geom_step() +
-    scale_x_log10() + scale_y_log10() +
-    xlab(xlab) + ylab(ylab) +
-    ggtitle("Reverse-cumulative plot")
+           , by="sampleLabels")
 
   if(!is.null(fitInRange)) {
     fit.coefs.m <- sapply(object, val.range = fitInRange, \(x, val.range)
@@ -138,20 +127,28 @@ setGeneric( "plotReverseCumulatives",
     reference.slope <- min(median(fit.slopes), -1.05)
     reference.library.size <- 10^floor(log10(median(sapply(object, sum))))
     reference.intercept <- log10(reference.library.size/VGAM::zeta(-1*reference.slope))  # intercept on log10 scale used for plotting with abline
-    p <- p +
-      geom_abline(intercept = reference.intercept, slope = reference.slope, color = "grey", linetype = "longdash") +
-      geom_vline(xintercept = fitInRange, color = "darkgrey", linetype = "dotted") +
-      scale_color_manual(
-        values = object@metadata$colData$Colors,
-        labels = paste0("(", formatC(-fit.slopes, format = "f", digits = 2), ") ",  names(fit.slopes))) +
-      labs(subtitle = paste0("Ref. distribution alpha = ", sprintf("%.2f", -reference.slope), ", T = ", reference.library.size, ".")) +
-      guides(col = guide_legend(title = "(alpha) sample names"))
-  } else {
-    p <- p +
-      scale_color_manual(values = object@metadata$colData$Colors)
-      guides(col = guide_legend(title = "Sample names"))
+
+    ggplot(intermediate_df) +
+      aes(x=x, y=y) +
+      geom_line() +
+      facet_wrap(. ~sampleLabels) +
+      scale_x_log10()  +
+      scale_y_log10()  +
+      labs(title="Reference distribution:",
+          subtitle = paste0("alpha= ", sprintf("%.2f", -1*reference.slope), " T= ", reference.library.size),
+          x =xlab, y = ylab) +
+      geom_text(data = intermediate_df,
+        mapping= aes(
+          x=10, y= 10,
+          label = paste0("alpha= ", formatC(-1*fit.slopes[sampleLabels], format = "f", digits = 2)))) +
+      geom_vline(xintercept=fitInRange, linetype="dotted") +
+      geom_abline(slope = reference.slope, intercept = reference.intercept,
+                          linetype="longdash", colour="#7F7F7F7F")
+  } else{
+    # ToDo: what is the intended behaviour?
+    warning("reference distribution not fitted")
   }
-  p + facet_wrap(group)
+
 }
 
 #' @rdname plotReverseCumulatives
@@ -230,8 +227,8 @@ setMethod("plotReverseCumulatives", "GRanges",
 #' @family CAGEr plot functions
 #' @family CAGEr clusters functions
 #' 
-#' @importFrom ggplot2 ggplot scale_fill_manual geom_histogram facet_wrap
-#' @importFrom ggplot2 ggtitle xlab ylab labs
+#' @importFrom ggplot2 ggplot aes_string scale_fill_manual geom_histogram facet_wrap
+#' @importFrom ggplot2 ggtitle xlab ylab
 #' 
 #' @examples
 #' 
@@ -278,17 +275,17 @@ setMethod( "plotInterquantileWidth", "CAGEexp"
 
 	binsize <- round(max(iqwidths$iq_width)/2)
 
-	ggplot2::ggplot(iqwidths) +
-	  ggplot2::aes_string(x = "iq_width", fill = "sampleName") +
-	  ggplot2::scale_fill_manual(values = names(sampleLabels(object))) +
-	  ggplot2::geom_histogram(bins = binsize) +
-	  ggplot2::facet_wrap("~sampleName") +
-	  ggplot2::ggtitle(paste0(
+	ggplot(iqwidths) +
+	  aes_string(x = "iq_width") +
+	  scale_fill_manual(values = names(sampleLabels(object))) +
+	  geom_histogram(bins = binsize) +
+	  facet_wrap("~sampleName") +
+	  ggtitle(paste0(
 	    switch(clusters, tagClusters = "Tag Clusters", consensusClusters = "Consenss Clusters"),
 	    " interquantile width (quantile ", qLow, " to ", qUp, ")")) +
-	  ggplot2::xlab("Interquantile width (bp)") +
-	  ggplot2::ylab("Frequency") +
-	  ggplot2::labs(fill = "Sample name")
+	  xlab("Interquantile width (bp)") +
+	  ylab("Frequency")
+
 })
 
 #' @name plotExpressionProfiles