library(ggplot2)
library(pheatmap)
library(RColorBrewer)
library(tidyverse)
library(ggpubr)
library(ggsignif)
library(openxlsx)
#library(viridis)
library(jcolors)
setwd("//atlas.uni.lux/users/isabel.rosety/GBA/DA ELISA")
#data <- read.csv("WB data for Rstudio plots.csv", header = T, sep = ",")
OutPath= "//atlas.uni.lux/users/isabel.rosety/GBA/DA ELISA"
data <- read.xlsx("Data combined for Plotting in R.xlsx")
#data15 <- read.xlsx("WB data for Rstudio plots.xlsx", sheet = 1) #15d are in sheet 1
#Day=as.character(DataCombined$Day)
Normalizing to the mean of the controls per feature
#Normalize to mean of controls for one feature
dataN <-data %>%
group_by(Batch, CellLine, Condition) %>%
mutate(DA.conc=DA.conc/mean(data$DA.conc[data$Condition=="CTRL"],na.rm = TRUE))
Plot for one feature
data %>%
ggplot(aes(x = Condition, y=DA.conc),ordered=TRUE)+
geom_boxplot(aes(fill=Condition),width=0.7)+
scale_fill_manual(values= c("#FFFFFF","#999999"),name = "Condition", guide = "none")+ #guide false will remove the legend for the condition
#ylim(4,9)+
#geom_boxplot(width=0.07, fill="white") +
geom_point(aes(color=CellLine),size=3,show.legend = T,alpha = 0.5)+
#scale_color_manual(values = rev(brewer.pal(n=6, name="OrRd")))+
scale_color_jcolors("pal7")+
#scale_color_viridis(option = "D", discrete=TRUE)+
#geom_point(shape = 1,size = 3,colour = "black")+
theme(legend.key=element_blank()) +
geom_signif(comparisons = list(c("CTRL", "GBA-PD")), test='wilcox.test',
vjust=0.5, size=0.5, textsize=9, map_signif_level=c("***"=0.001, "**"=0.01, "*"=0.05, " "=2) ) +
#ggpubr::stat_compare_means(comparisons=my_comparisons, method="wilcox.test", p.adjust.method="BH",label="p.signif", label.x = 1.5)+
labs(x ="",
y = "DA concentration (ng/mL)",
fill = "Condition",
title = "") +
theme_bw() +
theme(
axis.line = element_line(colour = 'black', size = 1) ,
axis.title.x = element_blank(),
axis.text.x = element_text(size=21, color="black"),
axis.title.y = element_text(size = 21),
axis.text.y = element_text(size=15, color="black"),
axis.ticks.y = element_line(),
axis.ticks.length=unit(.25, "cm"),
#change legend text font size)
#legend.key.size = unit(0.7, "cm"),
#legend.key.width = unit(0.6,"cm"),
legend.key=element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.title = element_text(size = 20, hjust=0.5, vjust= 1, face = "bold"),
plot.subtitle = element_blank(),#element_text(size = 2, hjust=0.5)
strip.text = element_text(size=12, vjust=0.5),
strip.background = element_rect(fill="lightgray"),
# panel.border = element_rect(fill = NA, color = "black"),
panel.spacing.y = unit(0.8, "lines"),
strip.switch.pad.wrap=unit(20, "lines"),
legend.position="right",
legend.text = element_text(size=17),
legend.title = element_text(size=19)
) -> p
#t<- cowplot::ggdraw(cowplot::add_sub(p, "Wilcox-test, ***p=0.001, **p=0.01, *p=0.05",hjust=-0.2, size=13))
print(p)

#t<- cowplot::ggdraw(cowplot::add_sub(p, "wilcox.test, ***p=0.001, **p=0.01, *p=0.05",hjust=-0.3, size=12))
#print(t)
ggsave(paste0(Sys.Date()," Dopamine levels DIV 60.pdf"), height=7, path=OutPath)
Saving 7.29 x 7 in image
Error in grDevices::pdf(file = filename, ..., version = version) :
cannot open file '//atlas.uni.lux/users/isabel.rosety/GBA/Organoids with Matrix/Stainings/Plots/2022-01-06 Dopamine levels DIV 60.pdf'
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