Updated Figure 2

f-generate_mean_sd_plot.R

@@ -20,13 +20,14 @@ trial <- c("Gallocatechin Gallate", "Genistein", "Imatinib",
 
 key <- c("Gemcitabine", "Trametinib", "Withaferin A", "Saracatinib", "Selumetinib", "Auranofin")
 
+
+
 data <-
   read_csv("results/ace2-summarized-dataset.csv", col_types = col_spec) %>%
   mutate(
     class = if_else((avg >= 0.47 & sdev <= 0.08), "novel", "filtered"),
     class = if_else(compound %in% antiviral, "antiviral", class),
-    class = if_else(compound %in% trial, "trial", class),
-    class = if_else(compound %in% key, "key", class),
+    class = if_else(compound %in% trial, "trial", class)
   ) %>%
   select(compound, avg, sdev, class) %>%
   filter(
@@ -42,30 +43,44 @@ data <-
     str_detect(compound, "^Tyrphostin", negate = T)
   )
 
+annotations <- data %>%
+  filter(compound %in% key) %>%
+  mutate(x = c(0.46, 0.56, 0.52, 0.6),
+         y = c(0.085, 0.05, 0.065, 0.04),
+         xend = avg + 0.00125,
+           yend = sdev + 0.0005,
+         label = compound)
+
+curve_annotations <- pmap(annotations, ~ annotate(geom = "curve", x = ..5, y = ..6, xend = ..7, yend = ..8, curvature = .3, arrow = arrow(length = unit(2, "mm"))))
+
+label_annotations <- pmap(annotations, ~ annotate(geom = "label", x = ..5, y = ..6, label = ..9, hjust = "center"))
+
+all_annotations <- c(curve_annotations, label_annotations)
+
 g <- ggplot(data, aes(x = avg, y = sdev, group = class, color = class, shape = class))
 
 p <- g + geom_point(size = 5) +
   scale_x_continuous(breaks = seq(0.32, 0.7, 0.02), limits = c(0.4, 0.7)) +
   scale_y_reverse(breaks = seq(0, 0.15, 0.01), limits = c(0.10, 0)) +
   geom_vline(xintercept = 0.47, color = "darkred", lwd = 1.5) +
   geom_hline(yintercept = 0.08, color = "darkred", lwd = 1.5) +
-  scale_color_manual(breaks = c("key", "antiviral", "trial", "novel", "filtered"),
-                     labels = c("Key Recommendations",
+  scale_color_manual(breaks = c("antiviral", "trial", "novel", "filtered"),
+                     labels = c(
                        "Known Anti-viral Properties",
                                 "Currently in trial",
                                 "Novel identified",
                                 "Did not pass Threshold"),
                      name = "Drug Status",
-                     values = c("darkred", "darkgreen", "maroon",
+                     values = c("darkgreen", "maroon",
                                 "darkblue", "lightgrey")) +
-  scale_shape_manual(breaks = c("key", "antiviral", "trial", "novel", "filtered"),
-                     labels = c("Key Recommendations",
+  scale_shape_manual(breaks = c("antiviral", "trial", "novel", "filtered"),
+                     labels = c(
                        "Known Anti-viral Properties",
                                 "Currently in trial",
                                 "Novel identified",
                                 "Did not pass Threshold"),
                      name = "Drug Status",
-                     values = c(25, 17, 18, 15, 16)) +
+                     values = c(17, 18, 15, 16)) +
   xlab("Mean Concordance") +
   ylab("Standard Deviation of Concordance") +
   theme_minimal() +
@@ -77,9 +92,12 @@ p <- g + geom_point(size = 5) +
     legend.title = element_text(size = 24, family = "ArialMT"),
     legend.key = element_rect(color = NA, fill = NA),
     legend.key.size = unit(1.5, "cm")
-  )
+  ) +
+  ggtitle("Concordance Plot of Identified Drugs")
+
+full <- reduce(all_annotations, `+`, .init = p)
+
 
-full <- p + ggtitle("Concordance Plot of Identified Drugs")
 
 ggsave("figures/SARS2-Mean-SD-Plot.png", plot = full, device = "png", width = 11.69 * 2, height = 8.27 * 2, units = "in")