Updated network files to v65 and split p-p m-m and p-m

Author: Luis Francisco Hernández Sánchez

docs/Architecture.pptx

@@ -1,64 +1,66 @@
-# This script plots the number of hit reactions and pathways for proteins and proteoforms
-
-###############################
-# Load libraries
-
-library(ggplot2)
-require(cowplot)
-library(stats)
-source("loadHits.R")
-
-###############################
-# First, load data
-
-hitsReactions <- load.hits(fileProteins = "ReactionsPerProtein.csv", fileProteoforms = "ReactionsPerProteoform.csv")
-hitsPathways <- load.hits(fileProteins = "PathwaysPerProtein.csv", fileProteoforms = "PathwaysPerProteoform.csv")
-
-hits <- load.hits.Merged(fileProteins = "HitsPerProtein.csv", fileProteoforms = "HitsPerProteoform.csv")
-
-###############################
-# Second, create plots
-
-cdat <- ddply(hitsPathways, "Type", summarise, Count.mean=mean(Count))
-cdat
-
-plot.density.ByAccession <- ggplot(hits[which(hits$Type == "Protein" & hits$Hit == "Pathway"),], aes(x=Count)) + 
-  scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9")) +
-  scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9")) +
-  geom_density(size=1, alpha = 0.4, show.legend = F) +
-  scale_x_log10(name = "# mapped pathways") +
-  theme_bw() +
-  geom_vline(xintercept=mean(hits$Count[which(hits$Type == "Protein" & hits$Hit == "Pathway")]), linetype="dashed", color = "black")
-plot.density.ByAccession
-
-plot.density.ByProteoform <- ggplot(hits[which(hits$Type == "Proteoform" & hits$Hit == "Pathway"),], aes(x=Count)) + 
-  scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9")) +
-  scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9")) +
-  geom_density(size=1, alpha = 0.4, show.legend = F) +
-  scale_x_log10(name = "# mapped pathways") +
-  theme_bw() +
-  geom_vline(xintercept=mean(hits$Count[which(hits$Type == "Proteoform" & hits$Hit == "Pathway")]), linetype="dashed", color = "black")
-plot.density.ByProteoform
-
-###############################
-# Third, put the plots together in a grid
-
-plot_grid(
-  plot.density.ByAccession, 
-  plot.density.ByProteoform,
-  plot.scatter,
-  labels = c("A", "B","C"))
-
-###############################
-# Fourth, create facets with densities
-
-plot.density <- ggplot(hits, aes(x=Count, colour = Type, fill = Type)) + 
-  facet_grid(. ~ Hit) +
-  scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9")) +
-  scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9")) +
-  geom_density(size=1, alpha = 0.4, show.legend = T) +
-  scale_x_continuous(limits = c(xMin, xMax)) +
-  theme_bw() + ggtitle(name = "# mapped pathways") +
-  geom_vline(data=cdat, aes(xintercept=Count.mean, color=Type),linetype="dashed", show.legend = T) 
-
-plot.density
+# This script plots the number of hit reactions and pathways for proteins and proteoforms
+
+###############################
+# Load libraries
+
+library(ggplot2)
+require(cowplot)
+library(stats)
+library(plyr)
+source("loadHits.R")
+
+###############################
+# First, load data
+
+hitsReactions <- load.hits(fileProteins = "ReactionsPerProtein.csv", fileProteoforms = "ReactionsPerProteoform.csv")
+hitsPathways <- load.hits(fileProteins = "PathwaysPerProtein.csv", fileProteoforms = "PathwaysPerProteoform.csv")
+
+hits <- load.hits.Merged(fileProteins = "HitsPerProtein.csv", fileProteoforms = "HitsPerProteoform.csv")
+
+###############################
+# Second, create plots
+
+
+cdat <- ddply(hitsPathways, "Type", summarise, Count.mean=mean(Count))
+cdat
+
+plot.density.ByAccession <- ggplot(hits[which(hits$Type == "Protein" & hits$Hit == "Pathway"),], aes(x=Count)) + 
+  scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9")) +
+  scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9")) +
+  geom_density(size=1, alpha = 0.4, show.legend = F) +
+  scale_x_log10(name = "# mapped pathways") +
+  theme_bw() +
+  geom_vline(xintercept=mean(hits$Count[which(hits$Type == "Protein" & hits$Hit == "Pathway")]), linetype="dashed", color = "black")
+plot.density.ByAccession
+
+plot.density.ByProteoform <- ggplot(hits[which(hits$Type == "Proteoform" & hits$Hit == "Pathway"),], aes(x=Count)) + 
+  scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9")) +
+  scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9")) +
+  geom_density(size=1, alpha = 0.4, show.legend = F) +
+  scale_x_log10(name = "# mapped pathways") +
+  theme_bw() +
+  geom_vline(xintercept=mean(hits$Count[which(hits$Type == "Proteoform" & hits$Hit == "Pathway")]), linetype="dashed", color = "black")
+plot.density.ByProteoform
+
+###############################
+# Third, put the plots together in a grid
+
+plot_grid(
+  plot.density.ByAccession, 
+  plot.density.ByProteoform,
+  plot.scatter,
+  labels = c("A", "B","C"))
+
+###############################
+# Fourth, create facets with densities
+
+plot.density <- ggplot(hits, aes(x=Count, colour = Type, fill = Type)) + 
+  facet_grid(. ~ Hit) +
+  scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9")) +
+  scale_color_manual(values=c("#999999", "#E69F00", "#56B4E9")) +
+  geom_density(size=1, alpha = 0.4, show.legend = T) +
+  scale_x_log10() +
+  theme_bw() +
+  geom_vline(data=cdat, aes(xintercept=Count.mean, color=Type),linetype="dashed", show.legend = T) 
+
+plot.density