library(ggplot2)
package 㤼㸱ggplot2㤼㸲 was built under R version 4.0.5
library(pheatmap)
package 㤼㸱pheatmap㤼㸲 was built under R version 4.0.5
library(RColorBrewer)
library(tidyverse)
package 㤼㸱tidyverse㤼㸲 was built under R version 4.0.5Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
-- Attaching packages ----------------------------------------------------------------------------------------------------------------------------------- tidyverse 1.3.1 --
v tibble  3.1.6     v dplyr   1.0.7
v tidyr   1.1.4     v stringr 1.4.0
v readr   2.1.1     v forcats 0.5.1
v purrr   0.3.4     
package 㤼㸱tibble㤼㸲 was built under R version 4.0.5package 㤼㸱tidyr㤼㸲 was built under R version 4.0.5package 㤼㸱readr㤼㸲 was built under R version 4.0.5package 㤼㸱dplyr㤼㸲 was built under R version 4.0.5package 㤼㸱forcats㤼㸲 was built under R version 4.0.5-- Conflicts -------------------------------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
library(ggpubr)
package 㤼㸱ggpubr㤼㸲 was built under R version 4.0.5
library(ggsignif)
package 㤼㸱ggsignif㤼㸲 was built under R version 4.0.5
library(openxlsx)
package 㤼㸱openxlsx㤼㸲 was built under R version 4.0.5
#library(viridis)
library(jcolors)
package 㤼㸱jcolors㤼㸲 was built under R version 4.0.5
setwd("//atlas.uni.lux/users/isabel.rosety/GBA/DA ELISA")
The working directory was changed to //atlas.uni.lux/users/isabel.rosety/GBA/DA ELISA inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
#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)) 

Plots

#Table_all_based_wideROS<-filter(Table_all_based_wideROS,Batch!="44")
#Table_all_based_wideROS<-filter(Table_all_based_wideROS,Batch=="52")
#Table_all_based_wideROS %>%
  #filter(!Condition%in%c("Control_DFX","PD_N370S_DFX")) -> Table_all_based_wideROS
#write.csv(Table_all_based_wideROS, file = 'Table_all_based_wideROS.csv')

data %>%
  ggplot(aes(x = Condition, y=DA.conc),ordered=TRUE)+
  #geom_violin( aes(fill=Condition),show.legend = T, trim=T),
  geom_violin( aes(fill=Condition),show.legend = T,scale = "width", trim=F)+
  geom_dotplot(binaxis = "y",stackdir = "center",dotsize=0.8)+
  #scale_fill_manual(values= c("#bdd7e7","#2171b5"),name = "Condition", guide = FALSE)+
  scale_fill_manual(values= alpha(c("#1565C0","#CC0000"),0.75),name = "Condition",guide = "none")+
  #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, margin_top=0.4,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 = 0.5) ,
    axis.title.x = element_blank(),
    axis.text.x = element_text(size=12, color="black"),
    axis.title.y = element_text(size = 12),
    axis.text.y = element_text(size=10, 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=3.5,width=3, path=OutPath)

Plot colored data points

```r


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

```r

  #t<- cowplot::ggdraw(cowplot::add_sub(p, \wilcox.test
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'
---
title: "R Notebook"
output: html_notebook
---
---
```{r}
library(ggplot2)
library(pheatmap)
library(RColorBrewer)
library(tidyverse)
library(ggpubr)
library(ggsignif)
library(openxlsx)
#library(viridis)
library(jcolors)
```

```{r}
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
```{r} 

#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)) 
```

Plots
```{r}
#Table_all_based_wideROS<-filter(Table_all_based_wideROS,Batch!="44")
#Table_all_based_wideROS<-filter(Table_all_based_wideROS,Batch=="52")
#Table_all_based_wideROS %>%
  #filter(!Condition%in%c("Control_DFX","PD_N370S_DFX")) -> Table_all_based_wideROS
#write.csv(Table_all_based_wideROS, file = 'Table_all_based_wideROS.csv')

data %>%
  ggplot(aes(x = Condition, y=DA.conc),ordered=TRUE)+
  #geom_violin( aes(fill=Condition),show.legend = T, trim=T),
  geom_violin( aes(fill=Condition),show.legend = T,scale = "width", trim=F)+
  geom_dotplot(binaxis = "y",stackdir = "center",dotsize=0.8)+
  #scale_fill_manual(values= c("#bdd7e7","#2171b5"),name = "Condition", guide = FALSE)+
  scale_fill_manual(values= alpha(c("#1565C0","#CC0000"),0.75),name = "Condition",guide = "none")+
  #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, margin_top=0.4,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 = 0.5) ,
    axis.title.x = element_blank(),
    axis.text.x = element_text(size=12, color="black"),
    axis.title.y = element_text(size = 12),
    axis.text.y = element_text(size=10, 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=3.5,width=3, path=OutPath)

```

Plot colored data points
```{r}


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=4, path=OutPath)
```


