AO_script
library(readxl)
library(wesanderson)
library(ggplot2)
library (boxplotdbl)
library (dplyr)
library(ggpubr)
library(rstatix)
data <-read_excel("AO_6_result.xlsx")
View (data)
head(data)
colnames(data) <- c("Condition","Mean/area")
print(colnames(data))
print(head(data))
data$Condition<-as.factor(data$Condition)
data$Mean/area<-as.numeric(data$Mean/area)
#data$Mean/area<-10e6*(data$Mean/area)
str(data)
krusty <- kruskal.test(Mean/area~Condition,data=data)
print(krusty)
custom_colors<-c("#f1bb7b","#fd6467", "#9A8822" ,"#F4A736", "#C93312","#899DA4","#FAEFD1" ,"#DC863B")
stats<-data%>%
group_by(Condition)%>%
summarise(
Q1= quantile(Mean/area,0.25),
Median= median(Mean/area),
Q3=quantile(Mean/area,0.75)
)
print(stats)
p <- ggplot(data, aes(x = Condition, y = Mean/area, fill=Condition)) +
geom_boxplot(outlier.colour = "#9C964AFF", outlier.shape = 21, outlier.size =2) +
scale_fill_manual(values = custom_colors)+
labs(x = "Conditions", y = "Fluorescence_particules/area") +
ggtitle("AO_PolMD_assay") +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5),
axis.title.x = element_text(margin = margin(t = 20)),
axis.title.y = element_text(margin = margin(r = 20))
)+
coord_cartesian(ylim = c(0,0.00005))
p + stat_compare_means(
method = "kruskal.test",
label = "p.signif",
label.y = max(data$Mean/area)
)
print(p) annotate("text", x=1.5, y=max(data$Méthylé)+7, label = paste("p-value", round(krusty$p.value,9)),size=3, color ="black") "#F4B5BDFF","#9C964AFF","#F8AFA8","#eccbae","#046c9a") custom_colors<-c("#0B775E","#C6CDF7")"#e6a0c4","#7294D4"("#C6CDF7","#0B775E")
posthoc <- dunn_test(Mean/area ~ Condition, data = data, p.adjust.method = "bonferroni")
print(posthoc) p + stat_compare_means( method = "wilcox.test", # Utilise le test de Wilcoxon pour les comparaisons par paires comparisons = combn(levels(data$Condition), 2, simplify = FALSE), # Comparer tous les groupes deux à deux p.adjust.method = "bonferroni" # Correction pour comparaisons multiples )
print(p)
posthoc <- dunn_test(Mean/area ~ Condition, data = data, p.adjust.method = "bonferroni")
posthoc_significant <- posthoc %>% filter(p < 0.05)
if (nrow(posthoc_significant) > 0) { p <- p + geom_text( data = posthoc_significant, aes(x = group1, y = 0.00004, label = paste("p =", signif(p, 4))), color = "black", size = 4 ) }
print(p)
posthoc <- dunn_test(Mean/area ~ Condition, data = data, p.adjust.method = "bonferroni")
print(posthoc) p + stat_compare_means( method = "wilcox.test", # Utilise le test de Wilcoxon pour les comparaisons par paires comparisons = combn(levels(data$Condition), 2, simplify = FALSE), # Comparer tous les groupes deux à deux p.adjust.method = "bonferroni" # Correction pour comparaisons multiples )