Updated figures.

f-generate_mean_sd_plot.R

@@ -11,18 +11,19 @@ col_spec <- cols(
 
 antiviral <- c("Gemcitabine", "Trametinib", "Withaferin A", "Saracatinib",
                "Erlotinib", "Alvocidib", "Itrazole", "Elesclomol",
-               "Dasatinib", "Panobinostat")
+               "Dasatinib", "Panobinostat", "Parthenolide", "Lapatinib",
+               "Sorafenib", "Auranofin", "Selumetinib")
 
 trial <- c("Gallocatechin Gallate", "Genistein", "Imatinib",
            "Dexamethasone Acetate", "Simvastatin", "Sirolimus",
            "Tamoxifen")
 
-key <- c("Gemcitabine", "Trametinib", "Withaferin A", "Saracatinib")
+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 >= 0.5 & sdev <= 0.06), "novel", "filtered"),
+    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),
@@ -46,8 +47,8 @@ g <- ggplot(data, aes(x = avg, y = sdev, group = class, color = class, shape = c
 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 = 0.5, color = "darkred", lwd = 1.5) +
-  geom_hline(yintercept = 0.06, color = "darkred", lwd = 1.5) +
+  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",
                        "Known Anti-viral Properties",

f-generate_upset_figure.R

@@ -9,10 +9,10 @@ col_spec <- cols(
   slog2 = col_double()
 )
 
-sars2 <- read_csv("results/sars2-summarized-dataset.csv", col_types = col_spec) %>%
+sars2 <- read_csv("results/ace2-summarized-dataset.csv", col_types = col_spec) %>%
   pull(compound)
 
-sars2_fda <-   read_csv("results/sars2-summarized-dataset.csv", col_types = col_spec) %>%
+sars2_fda <-   read_csv("results/ace2-summarized-dataset.csv", col_types = col_spec) %>%
   filter(str_detect(compound, "CHEMBL", negate = T),
                       str_detect(compound, "SCHEMBL", negate = T),
                       str_detect(compound, "^\\d+", negate = T),
@@ -71,9 +71,9 @@ dataset_unfiltered <- list(sars2 = sars2,
                            covidc = covidc,
                            covidm = covidm)
 
-dataset_fda <- list(`SARS-CoV2` = sars2_fda,
-                `China` = covidc_fda,
-                `Mt. Sinai` = covidm_fda)
+dataset_fda <- list(`GSE147507 CL` = sars2_fda,
+                `GSE145926` = covidc_fda,
+                `GSE147507 PS` = covidm_fda)
 
 #upset(fromList(dataset_all), nsets = length(dataset_all))
 #upset(fromList(dataset_unfiltered), nsets = length(dataset_unfiltered))