Figures valentina

figures/src/PathoFact_AMR_Figures.R

+# To generate figures for PathoFact output
+library(tidyverse)
+library(ggplot2)
+#################
+# Preprocessing #
+#################
+ONT <- read.csv("~/Documents/R_data/nanopore_ONT/pathofact_AMR/PathoFact_ONT.csv", row.names=1)
+# Toxin prediction
+Toxin_confidence_level <- c("-","1","2")
+Toxin_prediction <- c("non-pathogenic","secreted","non-secreted")
+Toxin <- data.frame(Toxin_confidence_level,Toxin_prediction)
+Toxin_ONT <- ONT %>% group_by(Toxin_confidence_level, Sample) %>% summarise(RNum_Gi=sum(RNum_Gi))
+Toxin_ONT <- left_join(Toxin_ONT, Toxin, by="Toxin_confidence_level")
+# Virulence prediction
+Virulence_confidence_level <- c("-","1","2","3","4")
+virulence_prediction <- c("non-pathogenic","secreted","non-secreted","non-pathogenic","non-pathogenic")
+Virulence <- data.frame(Virulence_confidence_level,virulence_prediction)
+Virulence_ONT <- ONT %>% group_by(Virulence_confidence_level, Sample) %>% summarise(RNum_Gi=sum(RNum_Gi))
+Virulence_ONT <- left_join(Virulence_ONT, Virulence)
+Virulence_ONT <- Virulence_ONT %>% group_by(virulence_prediction, Sample) %>% summarise(RNum_Gi=sum(RNum_Gi))
+# AMR prediction
+AMR_ONT <- ONT %>% select(10,15,16)
+AMR_ONT$AMR <- ifelse(AMR_ONT$AMR_Category == "-", "-", "AMR")
+## Overall AMR
+AMR_ONT_overall <- AMR_ONT %>% group_by(AMR, Sample) %>% summarise(RNum_Gi=sum(RNum_Gi))
+## AMR category
+AMR_ONT_category <- AMR_ONT %>% group_by(AMR_Category, Sample) %>% summarise(RNum_Gi=sum(RNum_Gi))
+
+ONT <- read.csv("~/Documents/R_data/nanopore_ONT/pathofact_AMR/PathoFact_ONT.csv", row.names=1)
+# Toxin prediction
+Toxin_confidence_level <- c("-","1","2")
+Toxin_prediction <- c("non-pathogenic","secreted","non-secreted")
+Toxin <- data.frame(Toxin_confidence_level,Toxin_prediction)
+Toxin_ONT <- ONT %>% group_by(Toxin_confidence_level, Sample) %>% summarise(RNum_Gi=sum(RNum_Gi))
+Toxin_ONT <- left_join(Toxin_ONT, Toxin, by="Toxin_confidence_level")
+# Virulence prediction
+Virulence_confidence_level <- c("-","1","2","3","4")
+virulence_prediction <- c("non-pathogenic","secreted","non-secreted","non-pathogenic","non-pathogenic")
+Virulence <- data.frame(Virulence_confidence_level,virulence_prediction)
+Virulence_ONT <- ONT %>% group_by(Virulence_confidence_level, Sample) %>% summarise(RNum_Gi=sum(RNum_Gi))
+Virulence_ONT <- left_join(Virulence_ONT, Virulence)
+Virulence_ONT <- Virulence_ONT %>% group_by(virulence_prediction, Sample) %>% summarise(RNum_Gi=sum(RNum_Gi))
+# AMR prediction
+AMR_ONT <- ONT %>% select(10,15,16)
+AMR_ONT$AMR <- ifelse(AMR_ONT$AMR_Category == "-", "-", "AMR")
+## Overall AMR
+AMR_ONT_overall <- AMR_ONT %>% group_by(AMR, Sample) %>% summarise(RNum_Gi=sum(RNum_Gi))
+## AMR category
+AMR_ONT_category <- AMR_ONT %>% group_by(AMR_Category, Sample) %>% summarise(RNum_Gi=sum(RNum_Gi))
+
+###########
+# Figures #
+###########
+#FIGURES
+#set theme
+theme_set(theme_bw() +
+            theme(strip.background = element_rect(colour = "black", fill = "white"), strip.text = element_text(size=14, family="Arial")) +
+            theme(axis.text.x = element_text(angle = 90, size = 12, family = "Arial", hjust = 0.95),
+                  axis.text.y = element_text(size = 14, family = "Arial"),
+                  axis.title.y = element_text(size = 12, family = "Arial"),
+                  axis.title.x=element_text(size = 14, face = "bold", family = "Arial"),
+                  axis.ticks.x=element_blank()) +
+            theme(plot.title = element_text(hjust = 0.5)) +
+            theme(legend.text = element_text(size = 9, family = "Arial"),
+                  legend.title = element_text(size = 9, family = "Arial", face = "bold"), legend.direction = "vertical", legend.box = "vertical")) 
+# Toxin Figure
+Toxin_ONT_figure <- Toxin_ONT %>% filter(Toxin_prediction != "non-pathogenic")
+Toxin_plot <- Toxin_ONT_figure %>% ggplot(aes(x=Sample, y=RNum_Gi, fill = Sample)) +
+  geom_bar(stat = "identity") +
+  facet_grid(~ Toxin_prediction) +
+  xlab(element_blank()) +
+  ylab("relative abundance (RNum_Gi)") +
+  ggtitle("Toxin") +
+  theme(legend.position = "none") # out comment for legend
+# Virulence Figure
+Virulence_ONT_figure <- Virulence_ONT %>% filter(virulence_prediction != "non-pathogenic")
+Virulence_plot <- Virulence_ONT_figure %>% ggplot(aes(x=Sample, y=RNum_Gi, fill = Sample)) +
+  geom_bar(stat = "identity") +
+  facet_grid(~ virulence_prediction) +
+  xlab(element_blank()) +
+  ylab("relative abundance (RNum_Gi)") +
+  ggtitle("Virulence") +
+  theme(legend.position = "none")
+# AMR overall Figure
+AMR_ONT_overall_Figure <- AMR_ONT_overall %>% filter(AMR != "-")
+AMR_plot <- AMR_ONT_overall_Figure %>% ggplot(aes(x=Sample, y=RNum_Gi, fill = Sample)) +
+  geom_bar(stat = "identity") +
+  facet_grid(~ AMR) +
+  xlab(element_blank()) +
+  ylab("relative abundance (RNum_Gi)") +
+  ggtitle("AMR")
+
+# Combine figures
+library(cowplot)
+plot_grid(Virulence_plot, Toxin_plot, AMR_plot, nrow = 1, labels = "auto")
+