library(ggplot2)
library(pheatmap)
library(RColorBrewer)
#display.brewer.all()
library(tidyverse)
library(ggpubr)
library(ggsignif)
library(openxlsx)
library(viridis)
library(data.table)
library(readxl)
library(janitor)
library(dplyr)
library(jcolors)
library(stringr)

Importing data

setwd("//atlas.uni.lux/users/isabel.rosety/GBA/Stainings/Plots/")
The working directory was changed to //atlas.uni.lux/users/isabel.rosety/GBA/Stainings/Plots 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.
#setwd("//atlas.uni.lux/LCSB_Cellular_Biology/16-Our Papers/In Preparation/GBA hMO_Isabel/Scripts/Data analysis/Image analysis data processing and plotting")

dir(path="//atlas.uni.lux/users/isabel.rosety/GBA/Stainings/Plots/DataFromImageAnalysis",full.names = TRUE) %>%
  map(~fread(.)) -> mytables #fread needs the whole path

#Merge list to merge all the tables of mytables into one big table
Table_All = bind_rows(mytables) 

#Adding column for condition
tmp=do.call(rbind,strsplit(Table_All$AreaName,"_")) #stringsplit will take the column Areaname and will split this depending on the underscore, then we put them otgeter one ofter the other
#tmp is a matrix, it is neither a dataframe nor a table, in a table you have to have column names, adding a row in a matrix is easier than in a table
#some areanames 2 underscores, others have 4 underscores, the number of maximum underscores defines the number of columns, so when only 2 underscores, it will fill the other 2 columns by repeating the value
Table_All$Condition = tmp[,1]

Table_All %>% 
   mutate(Condition = str_replace_all(Condition, 
            pattern = "MUT", replacement = "GBA-PD")) %>%
     mutate(Condition = str_replace_all(Condition, 
            pattern = "WT", replacement = "CTRL")) ->Table_All

#Selecting columns Markers
toselect = c("Barcode","AreaName","Condition","Day", "Well","Batch", "Tuj1ByNucAlive","THByNucAlive","THandTuj1ByTuj1","THFragmentation","THandTuj1ByNucAlive","THandMAP2ByMAP2","FOXA2ByNucAlive","THFragmentation","LinksBySkel","NodesBySkel","MAP2ByNucAlive","Ki67ByNucAlive", "Sox2ByNucAlive", "GFAPByNucAlive", "s100bByNucAlive", "MAP2ByNucAlive", "GFAPByS100b", "THMask","THMFI","MAP2Mask", "Sox2Mask","Ki67andSox2BySox2", "Sox2MFI", "OLIG2ByNucAlive", "OLIG2CytoPByNucAlive", "CNPaseByNucAlive" , "OLIG2NuclearByNucAlive", "DCXandNestinByDCX",  "DCX_NodesBySkel",  "DCX_LinksBySkel","DCXFragmentation", "NestinByNucAlive",   "DCXByNucAlive", "NestinAndDCXByNestin", "TotalNucMask", "DCXandNestinByNuc", "THMFI")


Table_All %>% #same 
  dplyr::select(toselect)->Table_All

Normalizing to the mean of the controls per feature

#Mean of replicates
feature_names <- colnames(Table_All[,7:ncol(Table_All)])
  Table_All%>%
    pivot_longer(feature_names,names_to = "Features", values_to = "Measure") -> Table_all_long
Table_all_long2 = drop_na(Table_all_long)  

Table_all_long2 %>%
  group_by(Day,Condition,AreaName,Batch,Features, Barcode) %>%
  summarize(MeanFeatures = mean(Measure)) -> Table_Mean
`summarise()` has grouped output by 'Day', 'Condition', 'AreaName', 'Batch', 'Features'. You can override using the `.groups` argument.
#  ungroup() %>% 
#  dplyr::select(-AreaName) %>% 
#  full_join(Table_all_long2, by="Features" ) 
    #full_join(Table_all_long2, by=c("Features")) %>% #if I wanted to normalize per WB membrane


#Normalizing to mean of controls
Table_Mean %>%
    dplyr::filter(Condition=="CTRL") %>% 
    #group_by(WB, Batch, Condition,Features) %>% 
    group_by(Condition, Day, Features,Batch) %>% 
    summarise(baseline = median(MeanFeatures)) %>% 
    ungroup() %>% 
    dplyr::select(-Condition) %>% 
    #dplyr::select(-Batch) %>%
    full_join(Table_Mean, by=c("Day","Features","Batch")) %>% 
    #full_join(Table_all_long2, by=c("Features")) %>% #if I wanted to normalize per WB membrane
    mutate(Foldchange = MeanFeatures /baseline) ->Table_all_based
`summarise()` has grouped output by 'Condition', 'Day', 'Features'. You can override using the `.groups` argument.
included_vars =c("Day", "AreaName", "Condition","Batch","Barcode") # here we already have the mean of each section
Table_all_based %>% 
    pivot_wider(all_of(included_vars),names_from = Features,values_from = Foldchange)->Table_all_based_wide2

write.csv(Table_all_based_wide2, file = 'Table_all_based_wide2.csv')

Table_all_based_wide2=Table_all_based_wide2[!grepl("D0",Table_all_based_wide2$Day),]

Table_all_based_wide15<-filter(Table_all_based_wide2,Day=="d15")
Table_all_based_wide30<-filter(Table_all_based_wide2,Day=="d30")
Table_all_based_wide60<-filter(Table_all_based_wide2,Day=="d60")
Table_all_based_wide90<-filter(Table_all_based_wide2,Day=="d90")



#Doing the mean if same marker is used for same timepoint in different barcodes
Table_all_based%>%
  group_by(Day,Condition,AreaName,Batch,Features) %>%
  summarize(MeanFeatures = mean(Foldchange)) -> Table_Mean_of_different_Barcodes
`summarise()` has grouped output by 'Day', 'Condition', 'AreaName', 'Batch'. You can override using the `.groups` argument.
included_vars =c("Day", "AreaName", "Condition","Batch") # here we already have the mean of each section
Table_Mean_of_different_Barcodes%>% 
    pivot_wider(all_of(included_vars),names_from = Features,values_from = MeanFeatures)->Table_Mean_of_different_Barcodes

write.csv(Table_Mean_of_different_Barcodes, file = 'Table_Mean_of_different_Barcodes.csv')

Plots for all the features one timepoint (loop)


feature_names <- colnames(Table_all_based_wide30[,6:ncol(Table_all_based_wide30)])

#Table_all_based_wide2=Table_all_based_wide2[!grepl("IR_20211129_488TH_568DCX_647MAP2_RS_e45e46e47d30_20211129_174319",Table_all_based_wide2$Barcode),]

#subset = c("Barcode","AreaName","Condition","Day","Batch","DCXandNestinByNuc")
#Table_all_based_wide2%>% #same 
#  dplyr::select(subset)->Table_all_based_wide3
#feature_names <- colnames(Table_all_based_wide3[,6:ncol(Table_all_based_wide3)])



for (i in 1:length(feature_names)) {
Table_all_based_wide30 %>%
  pivot_longer(cols=feature_names, names_to = "feature", values_to = "value") %>%
  filter(feature %in% feature_names[i]) %>%
ggplot( aes(x=factor(Condition, level = c("CTRL", "GBA-PD")), y=value)) +
  #geom_violin( aes(fill=Condition),show.legend = T, trim=T)+
  #scale_fill_manual(values= c("#bdd7e7","#2171b5"),name = "Condition", guide = FALSE)+
    scale_fill_manual(values= c("#FFFFFF","#999999"),name = "Condition", guide = "none")+ #guide false will remove the legend for the condition
    geom_boxplot(aes(fill=Condition),show.legend = FALSE,width=0.5)+ #show.legend will remove box around the points of the legend
    geom_point(aes(color=AreaName),size=3,show.legend = T,alpha = 0.5)+
    scale_color_jcolors("pal7")+
    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) ) +
  #facet_grid(~fct_relevel(Day, "d15", "d30","d60", "d90"), scales="free") +
      labs(x     = "",
       y     = paste(feature_names[i]),
       #y     = paste(names[i]),
       fill  = "Condition",
       #title = paste(feature_names[i])) +
       title = "" )+
  theme_bw() +
  theme(
    axis.line = element_line(colour = 'black', size = 0.5) ,
    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
  print(p)
#ggsave(paste0(Sys.Date(),(sprintf("Plot_%s_DIV30.pdf",feature_names[i]))),height=3.5)
}

Plots for all features all timepoints (loop)

#Table_all_based_wide2<-filter(Table_all_based_wide2,Barcode=="IR_20211016_488MAP2_568TUJ1_647TH_RS_e40e41e42d30_20211016_144439")

Table_all_based_wideT=Table_all_based_wide2[!grepl("D0",Table_all_based_wide2$Day),]
feature_names <- colnames(Table_all_based_wideT[,6:ncol(Table_all_based_wideT)])

#subset = c("Barcode","AreaName","Condition","Day","Batch","Tuj1ByNucAlive")
#Table_all_based_wideT%>% #same 
#  dplyr::select(subset)->Table_all_based_wideT2




for (i in 1:length(feature_names)) {
Table_all_based_wideT %>%
  pivot_longer(cols=feature_names, names_to = "feature", values_to = "value") %>%
  filter(feature %in% feature_names[i]) %>%
ggplot( aes(x=factor(Condition, level = c("CTRL", "GBA-PD")), y=value)) +
  #geom_violin( aes(fill=Condition),show.legend = T, trim=T)+

  #scale_fill_manual(values= c("#bdd7e7","#2171b5"),name = "Condition", guide = FALSE)+
    scale_fill_manual(values= c("#FFFFFF","#999999"),name = "Condition", guide = "none")+ #guide false will remove the legend for the condition
    #geom_boxplot(width=0.07, fill="white") + 
    geom_boxplot(aes(fill=Condition),show.legend = FALSE,width=0.7)+ #show.legend will remove box around the points of the legend
    geom_point(aes(color=AreaName),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=0.6, size=0.5, textsize=9, map_signif_level=c("***"=0.001, "**"=0.01, "*"=0.05,  " "=2) ) +
  facet_grid(~fct_relevel(Day, "d15", "d30","d60", "d90"), scales="free") +
      labs(x     = "",
       y     = paste(feature_names[i]),
       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",angle=30,vjust = 1.1, hjust = 0.95),
    axis.title.y = element_text(size = 15),
    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

  print(p)
 
ggsave(paste0(Sys.Date(),(sprintf("Plot_%s_DIV60 and DIV90.pdf",feature_names[i]))),height=4)
}

---
title: "R Notebook"
output: html_notebook
---
---
```{r}
library(ggplot2)
library(pheatmap)
library(RColorBrewer)
#display.brewer.all()
library(tidyverse)
library(ggpubr)
library(ggsignif)
library(openxlsx)
library(viridis)
library(data.table)
library(readxl)
library(janitor)
library(dplyr)
library(jcolors)
library(stringr)
```

Importing data
```{r}
setwd("//atlas.uni.lux/users/isabel.rosety/GBA/Stainings/Plots/")
#setwd("//atlas.uni.lux/LCSB_Cellular_Biology/16-Our Papers/In Preparation/GBA hMO_Isabel/Scripts/Data analysis/Image analysis data processing and plotting")

dir(path="//atlas.uni.lux/users/isabel.rosety/GBA/Stainings/Plots/DataFromImageAnalysis",full.names = TRUE) %>%
  map(~fread(.)) -> mytables #fread needs the whole path

#Merge list to merge all the tables of mytables into one big table
Table_All = bind_rows(mytables) 

#Adding column for condition
tmp=do.call(rbind,strsplit(Table_All$AreaName,"_")) #stringsplit will take the column Areaname and will split this depending on the underscore, then we put them otgeter one ofter the other
#tmp is a matrix, it is neither a dataframe nor a table, in a table you have to have column names, adding a row in a matrix is easier than in a table
#some areanames 2 underscores, others have 4 underscores, the number of maximum underscores defines the number of columns, so when only 2 underscores, it will fill the other 2 columns by repeating the value
Table_All$Condition = tmp[,1]

Table_All %>% 
   mutate(Condition = str_replace_all(Condition, 
            pattern = "MUT", replacement = "GBA-PD")) %>%
     mutate(Condition = str_replace_all(Condition, 
            pattern = "WT", replacement = "CTRL")) ->Table_All

#Selecting columns Markers
toselect = c("Barcode","AreaName","Condition","Day", "Well","Batch", "Tuj1ByNucAlive","THByNucAlive","THandTuj1ByTuj1","THFragmentation","THandTuj1ByNucAlive","THandMAP2ByMAP2","FOXA2ByNucAlive","THFragmentation","LinksBySkel","NodesBySkel","MAP2ByNucAlive","Ki67ByNucAlive", "Sox2ByNucAlive", "GFAPByNucAlive", "s100bByNucAlive", "MAP2ByNucAlive", "GFAPByS100b", "THMask","THMFI","MAP2Mask", "Sox2Mask","Ki67andSox2BySox2", "Sox2MFI", "OLIG2ByNucAlive", "OLIG2CytoPByNucAlive", "CNPaseByNucAlive" , "OLIG2NuclearByNucAlive", "DCXandNestinByDCX",	"DCX_NodesBySkel",	"DCX_LinksBySkel","DCXFragmentation", "NestinByNucAlive",	"DCXByNucAlive", "NestinAndDCXByNestin", "TotalNucMask", "DCXandNestinByNuc", "THMFI")


Table_All %>% #same 
  dplyr::select(toselect)->Table_All

```

Normalizing to the mean of the controls per feature
```{r} 
#Mean of replicates
feature_names <- colnames(Table_All[,7:ncol(Table_All)])
  Table_All%>%
    pivot_longer(feature_names,names_to = "Features", values_to = "Measure") -> Table_all_long
Table_all_long2 = drop_na(Table_all_long)  

Table_all_long2 %>%
  group_by(Day,Condition,AreaName,Batch,Features, Barcode) %>%
  summarize(MeanFeatures = mean(Measure)) -> Table_Mean


#  ungroup() %>% 
#  dplyr::select(-AreaName) %>% 
#  full_join(Table_all_long2, by="Features" ) 
    #full_join(Table_all_long2, by=c("Features")) %>% #if I wanted to normalize per WB membrane


#Normalizing to mean of controls
Table_Mean %>%
    dplyr::filter(Condition=="CTRL") %>% 
    #group_by(WB, Batch, Condition,Features) %>% 
    group_by(Condition, Day, Features,Batch) %>% 
    summarise(baseline = median(MeanFeatures)) %>% 
    ungroup() %>% 
    dplyr::select(-Condition) %>% 
    #dplyr::select(-Batch) %>%
    full_join(Table_Mean, by=c("Day","Features","Batch")) %>% 
    #full_join(Table_all_long2, by=c("Features")) %>% #if I wanted to normalize per WB membrane
    mutate(Foldchange = MeanFeatures /baseline) ->Table_all_based

included_vars =c("Day", "AreaName", "Condition","Batch","Barcode") # here we already have the mean of each section
Table_all_based %>% 
    pivot_wider(all_of(included_vars),names_from = Features,values_from = Foldchange)->Table_all_based_wide2

#write.csv(Table_all_based_wide2, file = 'Table_all_based_wide2.csv')

Table_all_based_wide2=Table_all_based_wide2[!grepl("D0",Table_all_based_wide2$Day),]

Table_all_based_wide15<-filter(Table_all_based_wide2,Day=="d15")
Table_all_based_wide30<-filter(Table_all_based_wide2,Day=="d30")
Table_all_based_wide60<-filter(Table_all_based_wide2,Day=="d60")
Table_all_based_wide90<-filter(Table_all_based_wide2,Day=="d90")



#Doing the mean if same marker is used for same timepoint in different barcodes
Table_all_based%>%
  group_by(Day,Condition,AreaName,Batch,Features) %>%
  summarize(MeanFeatures = mean(Foldchange)) -> Table_Mean_of_different_Barcodes
included_vars =c("Day", "AreaName", "Condition","Batch") # here we already have the mean of each section
Table_Mean_of_different_Barcodes%>% 
    pivot_wider(all_of(included_vars),names_from = Features,values_from = MeanFeatures)->Table_Mean_of_different_Barcodes

#write.csv(Table_Mean_of_different_Barcodes, file = 'Table_Mean_of_different_Barcodes.csv')

```

Plots for all the features one timepoint (loop)
```{r}

feature_names <- colnames(Table_all_based_wide30[,6:ncol(Table_all_based_wide30)])

#Table_all_based_wide2=Table_all_based_wide2[!grepl("IR_20211129_488TH_568DCX_647MAP2_RS_e45e46e47d30_20211129_174319",Table_all_based_wide2$Barcode),]

#subset = c("Barcode","AreaName","Condition","Day","Batch","DCXandNestinByNuc")
#Table_all_based_wide2%>% #same 
#  dplyr::select(subset)->Table_all_based_wide3
#feature_names <- colnames(Table_all_based_wide3[,6:ncol(Table_all_based_wide3)])



for (i in 1:length(feature_names)) {
Table_all_based_wide30 %>%
  pivot_longer(cols=feature_names, names_to = "feature", values_to = "value") %>%
  filter(feature %in% feature_names[i]) %>%
ggplot( aes(x=factor(Condition, level = c("CTRL", "GBA-PD")), y=value)) +
  #geom_violin( aes(fill=Condition),show.legend = T, trim=T)+
  #scale_fill_manual(values= c("#bdd7e7","#2171b5"),name = "Condition", guide = FALSE)+
    scale_fill_manual(values= c("#FFFFFF","#999999"),name = "Condition", guide = "none")+ #guide false will remove the legend for the condition
    geom_boxplot(aes(fill=Condition),show.legend = FALSE,width=0.5)+ #show.legend will remove box around the points of the legend
    geom_point(aes(color=AreaName),size=3,show.legend = T,alpha = 0.5)+
    scale_color_jcolors("pal7")+
    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) ) +
  #facet_grid(~fct_relevel(Day, "d15", "d30","d60", "d90"), scales="free") +
      labs(x     = "",
       y     = paste(feature_names[i]),
       #y     = paste(names[i]),
       fill  = "Condition",
       #title = paste(feature_names[i])) +
       title = "" )+
  theme_bw() +
  theme(
    axis.line = element_line(colour = 'black', size = 0.5) ,
    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
  print(p)
#ggsave(paste0(Sys.Date(),(sprintf("Plot_%s_DIV30.pdf",feature_names[i]))),height=3.5)
}
```

Plots for all features all timepoints (loop)
```{r}
#Table_all_based_wide2<-filter(Table_all_based_wide2,Barcode=="IR_20211016_488MAP2_568TUJ1_647TH_RS_e40e41e42d30_20211016_144439")

Table_all_based_wideT=Table_all_based_wide2[!grepl("D0",Table_all_based_wide2$Day),]
feature_names <- colnames(Table_all_based_wideT[,6:ncol(Table_all_based_wideT)])

#subset = c("Barcode","AreaName","Condition","Day","Batch","Tuj1ByNucAlive")
#Table_all_based_wideT%>% #same 
#  dplyr::select(subset)->Table_all_based_wideT2




for (i in 1:length(feature_names)) {
Table_all_based_wideT %>%
  pivot_longer(cols=feature_names, names_to = "feature", values_to = "value") %>%
  filter(feature %in% feature_names[i]) %>%
ggplot( aes(x=factor(Condition, level = c("CTRL", "GBA-PD")), y=value)) +
  #geom_violin( aes(fill=Condition),show.legend = T, trim=T)+

  #scale_fill_manual(values= c("#bdd7e7","#2171b5"),name = "Condition", guide = FALSE)+
    scale_fill_manual(values= c("#FFFFFF","#999999"),name = "Condition", guide = "none")+ #guide false will remove the legend for the condition
    #geom_boxplot(width=0.07, fill="white") + 
    geom_boxplot(aes(fill=Condition),show.legend = FALSE,width=0.7)+ #show.legend will remove box around the points of the legend
    geom_point(aes(color=AreaName),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=0.6, size=0.5, textsize=9, map_signif_level=c("***"=0.001, "**"=0.01, "*"=0.05,  " "=2) ) +
  facet_grid(~fct_relevel(Day, "d15", "d30","d60", "d90"), scales="free") +
      labs(x     = "",
       y     = paste(feature_names[i]),
       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",angle=30,vjust = 1.1, hjust = 0.95),
    axis.title.y = element_text(size = 15),
    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

  print(p)
 
#ggsave(paste0(Sys.Date(),(sprintf("Plot_%s_All timepoints.pdf",feature_names[i]))),height=4)
}
```

