Script modification

paper/analysis/200317-CodonOptimizationVSoptimality/02-plot-compare.R

@@ -5,13 +5,13 @@ library(ggthemes)
 theme_set(theme_tufte(base_family = "Helvetica"))
 
 optimality <- read_csv("results_data/optimality_lasso.csv")
-usage <- read_csv("results_data/codon_usage.csv") 
+usage <- read_csv("results_data/codon_usage.csv")
 
-datos <- inner_join(optimality, usage) %>% 
+datos <- inner_join(optimality, usage) %>%
   filter(!codon %in% c("TAG", "TAA", "TGA"))
 
-datos %>% 
-  mutate(specie = factor(specie, levels = c("fish", "xenopus", "mouse", "human"))) %>% 
+datos %>%
+  mutate(specie = factor(specie, levels = c("fish", "xenopus", "mouse", "human"))) %>%
   ggplot(aes(x=lasso_coef, y=usage)) +
   geom_point(color="grey") +
   scale_y_log10() +
@@ -21,3 +21,45 @@ datos %>%
   geom_smooth(method = "lm", linetype=2, color="black", size=1/10, se = F) +
   ggpubr::stat_cor()
 ggsave("optimalityVSusage.pdf", height = 6, width = 6)
+
+
+# compare with the csc ----------------------------------------------------
+
+csc_scores <- read_csv("https://elifesciences.org/download/aHR0cHM6Ly9jZG4uZWxpZmVzY2llbmNlcy5vcmcvYXJ0aWNsZXMvNDUzOTYvZWxpZmUtNDUzOTYtZmlnMS1kYXRhMi12Mi5jc3Y=/elife-45396-fig1-data2-v2.csv?_hash=nbm%2BWR4OoqoKYxyrjy3d3MXcEN9Etr0iBEb1OoLnRes%3D")
+
+csc_scores <- csc_scores %>%
+  select(codon:K562_SLAM) %>%
+  select(-contains('ORFome')) %>%
+  gather(key = profile, value = csc, -codon, -Name) %>%
+  group_by(codon, Name) %>%
+  summarise(CSC = median(csc))
+
+
+csc_scores <-
+  usage %>%
+  filter(specie == "human") %>%
+  inner_join(csc_scores)
+
+csc_scores %>%
+  ggplot(aes(x=usage, y=CSC)) +
+  geom_point() +
+  ggrepel::geom_text_repel(aes(label=codon)) +
+  scale_x_log10() +
+  ggpubr::stat_cor()
+
+csc_scores %>%
+  filter(Name %in% c("Arg", "Thr")) %>%
+  ggplot(aes(x=CSC, y=usage)) +
+  geom_point(shape = 16, size = 2, alpha =  .99, color = "steelblue") +
+  geom_text_repel(aes(label = codon), size=3, color = 'grey30') +
+  scale_x_continuous(breaks = c(-.1, 0, .1)) +
+  scale_y_continuous(labels = scales::percent) +
+  facet_wrap(~Name) +
+  geom_rangeframe(size=1/4) +
+  ggpubr::stat_cor(method = 'spearman', size = 3, color = "grey20") +
+  labs(
+    x = "Codon Stabilization Coefficient",
+    y = "Codon usage"
+  )
+ggsave("usageVScsc.pdf", height = 2, width = 4)
+