generalize density calculation

R/calculate.density.R

@@ -0,0 +1,19 @@
+calculate.density <- function(
+    x,
+    value = 'genome.pos',
+    group = 'clone.id',
+    scale = TRUE,
+    ...
+    ) {
+
+    if (nrow(x) <= 1) {
+        return(NULL);
+        }
+    density <- density(x = x[[value]], bw = 'nrd', na.rm = TRUE, ...);
+    density.df <- as.data.frame(density[c('x', 'y')]);
+    density.df$clone.id <- unique(x[[group]]);
+    if (scale) {
+        density.df$y <- nrow(x) / sum(density.df$y) * density.df$y;
+        }
+    return(density.df)
+    }

R/create.clone.genome.distribution.densityplot.R

@@ -8,7 +8,7 @@ create.clone.genome.distribution.densityplot <- function(
 
     return(BoutrosLab.plotting.general::create.scatterplot(
         filename = save.plt,
-        formula = count ~ x,
+        formula = y ~ x,
         data = density.df,
         groups = density.df$clone.id,
         xlab.label = 'Chromosome',
@@ -24,17 +24,3 @@ create.clone.genome.distribution.densityplot <- function(
         ...
         ));
     }
-
-calculate.density.and.scale <- function(
-    x,
-    value = 'genome.pos',
-    group = 'clone.id'
-    ) {
-
-    density <- density(x = x[[value]], bw = 'nrd', adjust = 0.05, na.rm = TRUE);
-    density.df <- as.data.frame(density[c('x', 'y')]);
-    density.df$clone.id <- unique(x[[group]]);
-    density.df$count <- nrow(x) / sum(density.df$y) * density.df$y;
-
-    return(density.df)
-    }

R/create.clone.genome.distribution.plot.R

@@ -104,8 +104,9 @@ create.clone.genome.distribution.plot.per.sample <- function(
             warning(paste('Skipping clone', k, 'in sample', unique(sample.df$ID), 'since there is only one SNV'));
             next;
         }
-        density.list[[k]] <- calculate.density.and.scale(
-            x = sample.df[sample.df$clone.id == k, ]
+        density.list[[k]] <- calculate.density(
+            x = sample.df[sample.df$clone.id == k, ],
+            adjust = 0.05
             );
         }
     density.df <- do.call(rbind, density.list);