v2.0.2

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: scRepertoire
 Title: A toolkit for single-cell immune receptor profiling
-Version: 2.0.0
+Version: 2.0.2
 Authors@R: c(
     person(given = "Nick", family = "Borcherding", role = c("aut", "cre"), email = "[email protected]"), 
     person(given = "Qile", family = "Yang", role = c("aut"), email = "[email protected]"), 

NEWS.md

@@ -1,3 +1,14 @@
+# scRepertoire VERSION 2.0.2
+
+## UNDERLYING CHANGES
+
+*```clonalOccupy()``` rewrite counting and NA handling
+
+# scRepertoire VERSION 2.0.1 
+
+## UNDERLYING CHANGES
+
+*```clonalOverlay()``` arguments now cutpoint and use cut.category to select either clonalProportion or clonalFrequency
 
 # scRepertoire VERSION 2.0.0 (2024-01-10)
 

R/clonalOccupy.R

@@ -33,7 +33,7 @@
 #' environment in addition to the visualization.
 #' @param palette Colors to use in visualization - input any 
 #' \link[grDevices]{hcl.pals}.
-#' @importFrom dplyr %>% group_by mutate
+#' @importFrom dplyr %>% group_by mutate count
 #' @importFrom reshape2 melt
 #' @import ggplot2
 #' @export
@@ -50,20 +50,30 @@
                          palette = "inferno") {
   .checkSingleObject(sc.data)
   meta <- .grabMeta(sc.data)
-  meta <- melt(table(meta[!is.na(meta[,"clonalFrequency"]), 
-                          c(x.axis, facet.by, "cloneSize")], useNA = "ifany"))
+
+  meta <- meta %>%
+            group_by(meta[,x.axis], meta[,facet.by], cloneSize) %>%
+            count() %>%
+            as.data.frame()
+  meta[,1] <- as.factor(meta[,1])
+
+  colnames(meta)[1] <- x.axis
+  if(!is.null(facet.by)) {
+    colnames(meta)[2] <- facet.by
+  }
+
   #Check for NAs
   if (!na.include) {
     meta <- na.omit(meta)
   }
-  meta <- meta[meta$value != 0,]
+  meta <- meta[meta$n != 0,]
 
   #Convert to proportion
   if(proportion) {
     meta <- meta %>%
       group_by(meta[,1]) %>%
-      mutate(total = sum(value), 
-             prop = value/total)
+      mutate(total = sum(n), 
+             prop = n/total)
     meta <- as.data.frame(meta)
   }
   if (exportTable) {
@@ -77,7 +87,7 @@
     lab <- "Proportion of Cells"
 
   } else {
-    plot <- ggplot(meta, aes(x = meta[,x.axis], y = value, fill = cloneSize)) + 
+    plot <- ggplot(meta, aes(x = meta[,x.axis], y = n, fill = cloneSize)) + 
               geom_bar(stat = "identity", color = "black", lwd = 0.25) 
     lab <- "Single Cells"
 
@@ -94,7 +104,7 @@
   }
   if (label) {
     plot <- plot + 
-              geom_text(aes(label = value), position = position_stack(vjust = 0.5))
+              geom_text(aes(label = n), position = position_stack(vjust = 0.5))
   }
   plot
 }

R/clonalOverlay.R

@@ -20,13 +20,15 @@
 #' #Using clonalOverlay()
 #' clonalOverlay(scRep_example, 
 #'               reduction = "umap", 
-#'               freq.cutpoint = 0.3, 
+#'               cutpoint = 3, 
 #'               bins = 5) 
 #' 
 #' @param sc.data The single-cell object after \code{\link{combineExpression}}.
-#' @param reduction The dimensional reduction to visualize
-#' @param freq.cutpoint The overlay cut point to include, this corresponds to the 
-#' Frequency variable in the single-cell object
+#' @param reduction The dimensional reduction to visualize.
+#' @param cut.category Meta data variable of the single-cell object to use for 
+#' filtering.
+#' @param cutpoint The overlay cut point to include, this corresponds to the 
+#' cut.category variable in the meta data of the single-cell object.
 #' @param bins The number of contours to the overlay
 #' @param facet.by meta data variable to facet the comparison
 #' 
@@ -40,20 +42,28 @@
 
 clonalOverlay <- function(sc.data, 
                           reduction = NULL, 
-                          freq.cutpoint = 30, 
+                          cut.category = "clonalFrequency",
+                          cutpoint = 30, 
                           bins = 25, 
                           facet.by = NULL) {
   .checkSingleObject(sc.data)
 
   #Forming the data frame to plot
   tmp <- data.frame(.grabMeta(sc.data), .get.coord(sc.data, reduction))
+
+  if(cut.category %!in% colnames(tmp)) {
+    stop("If filtering the data using a cutpoint, ensure the cut.category correspond to a variable in the meta data.")
+  }
   #Add facet variable if present
   if (!is.null(facet.by)) { 
     facet.by <- tmp[,facet.by]
     tmp <- data.frame(facet.by, tmp)
   }
-  tmp$include <- ifelse(tmp[,"clonalFrequency"] >= freq.cutpoint, "Yes", NA)
-  tmp2 <- subset(tmp, include == "Yes")
+  #If using cut.category for filtering
+  if(!is.null(cut.category) & !is.null(cutpoint)) {
+    tmp$include <- ifelse(tmp[,cut.category] >= cutpoint, "Yes", NA)
+    tmp2 <- subset(tmp, include == "Yes")
+  }
 
   #Plotting
   plot <- ggplot(tmp2, mapping = aes(x = tmp2[,(ncol(tmp2)-2)],