library(jcolors)
package 㤼㸱jcolors㤼㸲 was built under R version 4.0.5
setwd("//atlas.uni.lux/users/isabel.rosety/GBA/PI_FACS")
#data <- read.csv("WB data for Rstudio plots.csv", header = T, sep = ",")
OutPath= "//atlas.uni.lux/users/isabel.rosety/GBA/PI_FACS"
data <- read.xlsx("All samples.xlsx",sheet=2)
#data15 <- read.xlsx("WB data for Rstudio plots.xlsx", sheet = 1) #15d are in sheet 1
#Day=as.character(DataCombined$Day)
Mean of replicates - No normalization
#Mean of replicates
feature_names <- colnames(data[,5:ncol(data)])
data%>%
pivot_longer(feature_names,names_to = "Features", values_to = "Measure") -> Table_all_long
Table_all_long = drop_na(Table_all_long)
Table_all_long %>%
group_by(Condition,Batch,Features,CellLine) %>%
summarize(MeanFeatures = mean(Measure)) -> Table_Mean
`summarise()` has grouped output by 'Condition', 'Batch', 'Features'. You can override using the `.groups` argument.
included_vars =c("Batch", "CellLine", "Condition")
Table_Mean %>%
pivot_wider(all_of(included_vars),names_from = Features,values_from = MeanFeatures)->Table_Mean_wide
Mean of replicates & normalizaton to controls
#Mean of replicates
feature_names <- colnames(data[,5:ncol(data)])
data%>%
pivot_longer(feature_names,names_to = "Features", values_to = "Measure") -> Table_all_long
Table_all_long = drop_na(Table_all_long)
Table_all_long %>%
group_by(Condition,Batch,Features,CellLine) %>%
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=="Control") %>%
group_by(Batch,Features, Condition) %>%
#group_by(Features, Batch) %>%
summarise(baseline = mean(MeanFeatures)) %>%
ungroup() %>%
dplyr::select(-Condition) %>%
#dplyr::select(-Batch) %>%
full_join(Table_Mean, by=c("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("Batch", "CellLine", "Condition")
Table_all_based %>%
pivot_wider(all_of(included_vars),names_from = Features,values_from = Foldchange)->Table_all_based_wide
Table_all_based_wide<-filter(Table_all_based_wide,Batch!="46")
Table_all_based_wide %>%
filter(!Condition%in%c("Control_DFX","GBA-PD_DFX")) -> Table_all_based_wide
Table_Mean_wide<-filter(Table_Mean_wide,Batch!="46")
for (i in 1:length(feature_names)) {
Table_Mean_wide %>%
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=0.5)+ #show.legend will remove box around the points of the legend
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) ) +
#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
#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)
##ggsave(paste0(Sys.Date(),"_", names[i], ".pdf"), plot=t)
ggsave(paste0(Sys.Date(),(sprintf("Plot_%s_DIV30.pdf",feature_names[i]))),height=6)
}




Normalization if mean of duplicates already done
data%>%
pivot_longer(feature_names,names_to = "Features", values_to = "Measure") -> Table_all_long
Table_all_long2 = drop_na(Table_all_long)
Table_all_long2 %>%
dplyr::filter(Condition=="Control") %>%
#group_by(WB, Batch, Condition,Features) %>%
group_by(Condition,Features) %>%
summarise(baseline = median(Measure)) %>%
ungroup() %>%
dplyr::select(-Condition) %>%
#full_join(Table_all_long2, by="Features" ) %>%
full_join(Table_all_long2, by=c("Features")) %>% #To normalize per timepoint
mutate(Foldchange = Measure /baseline) ->Table_all_based
#subset(Table_all_based,duplicated(Measure)) #to see duplicated rows
included_vars =c("Batch", "CellLine", "Condition")
Table_all_based %>%
pivot_wider(all_of(included_vars),names_from = Features,values_from = Foldchange)->Table_all_based_wide
Plot for one feature
data %>%
ggplot(aes(x = Condition, y=Gcase.activity),ordered=TRUE)+
geom_boxplot(aes(fill=Condition),width=0.5)+
scale_fill_manual(values=c("#2171b5","#B22222","#008B8B"))+ #blue and red
ylim(4,9)+
##geom_point(aes(color=cell_line, shape=Section), size=5)
geom_point(aes(color=CellLine),size=3)+
scale_color_viridis(option = "D", discrete=TRUE)+
geom_point(shape = 1,size = 3,colour = "black")+
#facet_grid(fct_relevel(Day, "15", "60"), scales="free") +
labs(x ="",
y = "Relative GCase activity",
fill = "Condition",
title = "GCase activity") +
#stat_compare_means(comparisons=list(c("Control", "PD N370S")), method = "t.test", label="p.signif", label.x = 1.5)+
stat_compare_means(comparisons=list(c("Control", "PD-N370S")), method = "wilcox.test", label="p.signif", label.x = 1.5, size=7)+
#ggpubr::stat_compare_means(comparisons=my_comparisons, method="wilcox.test", p.adjust.method="BH",label="p.signif", label.x = 1.5)+
theme_bw() +
theme(plot.title=element_text(size=20, face="bold", hjust=0.5),
axis.text.x=element_text(size = 17, face = "bold",angle=45, hjust=1),
axis.text.y=element_text(size = 16),
axis.title.x=element_blank(),
axis.title.y=element_text(size=20),
legend.title = element_text(size=20),
legend.text = element_text(size=16))-> p #change legend text font size)
t<- cowplot::ggdraw(cowplot::add_sub(p, "wilcox.test, ***p=0.001, **p=0.01, *p=0.05",hjust=-0.1, size=15))
print(t)
ggsave(paste0(Sys.Date()," GCase activity DIV30.pdf"), plot=t, height=7, width=6*1.2, path=OutPath)
---
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/PI_FACS")
#data <- read.csv("WB data for Rstudio plots.csv", header = T,  sep = ",")
OutPath= "//atlas.uni.lux/users/isabel.rosety/GBA/PI_FACS"
data <- read.xlsx("All samples.xlsx",sheet=2)
#data15 <- read.xlsx("WB data for Rstudio plots.xlsx", sheet = 1) #15d are in sheet 1
#Day=as.character(DataCombined$Day)
```

Mean of replicates - No normalization
```{r}

#Mean of replicates
feature_names <- colnames(data[,5:ncol(data)])
  data%>%
    pivot_longer(feature_names,names_to = "Features", values_to = "Measure") -> Table_all_long
Table_all_long = drop_na(Table_all_long)  

Table_all_long %>%
  group_by(Condition,Batch,Features,CellLine) %>%
  summarize(MeanFeatures = mean(Measure)) -> Table_Mean

included_vars =c("Batch", "CellLine", "Condition")
Table_Mean %>% 
    pivot_wider(all_of(included_vars),names_from = Features,values_from = MeanFeatures)->Table_Mean_wide

```

Mean of replicates & normalizaton to controls
```{r}

  #Mean of replicates
feature_names <- colnames(data[,5:ncol(data)])
  data%>%
    pivot_longer(feature_names,names_to = "Features", values_to = "Measure") -> Table_all_long
Table_all_long = drop_na(Table_all_long)  

Table_all_long %>%
  group_by(Condition,Batch,Features,CellLine) %>%
  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=="Control") %>% 
    group_by(Batch,Features, Condition) %>% 
    #group_by(Features, Batch) %>% 
    summarise(baseline = mean(MeanFeatures)) %>% 
    ungroup() %>% 
    dplyr::select(-Condition) %>% 
    #dplyr::select(-Batch) %>%
    full_join(Table_Mean, by=c("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("Batch", "CellLine", "Condition")
Table_all_based %>% 
    pivot_wider(all_of(included_vars),names_from = Features,values_from = Foldchange)->Table_all_based_wide
```

```{r}
Table_all_based_wide<-filter(Table_all_based_wide,Batch!="46")
Table_all_based_wide %>%
  filter(!Condition%in%c("Control_DFX","GBA-PD_DFX")) -> Table_all_based_wide

Table_Mean_wide<-filter(Table_Mean_wide,Batch!="46")

for (i in 1:length(feature_names)) {
Table_Mean_wide %>%
  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=0.5)+ #show.legend will remove box around the points of the legend
    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) ) +
  #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
  #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)
  ##ggsave(paste0(Sys.Date(),"_", names[i], ".pdf"), plot=t)
ggsave(paste0(Sys.Date(),(sprintf("Plot_%s_DIV30.pdf",feature_names[i]))),height=6)
}
```

Normalization if mean of duplicates already done
```{r}
  data%>%
    pivot_longer(feature_names,names_to = "Features", values_to = "Measure") -> Table_all_long
Table_all_long2 = drop_na(Table_all_long)  
Table_all_long2 %>% 
    dplyr::filter(Condition=="Control") %>% 
    #group_by(WB, Batch, Condition,Features) %>% 
    group_by(Condition,Features) %>% 
    summarise(baseline = median(Measure)) %>% 
    ungroup() %>% 
    dplyr::select(-Condition) %>% 
    #full_join(Table_all_long2, by="Features" ) %>% 
    full_join(Table_all_long2, by=c("Features")) %>% #To normalize per timepoint
    mutate(Foldchange = Measure /baseline) ->Table_all_based

#subset(Table_all_based,duplicated(Measure)) #to see duplicated rows

included_vars =c("Batch", "CellLine", "Condition")
Table_all_based %>% 
    pivot_wider(all_of(included_vars),names_from = Features,values_from = Foldchange)->Table_all_based_wide

```

Plot for one feature
```{r}

data %>%
  ggplot(aes(x = Condition, y=Gcase.activity),ordered=TRUE)+
    geom_boxplot(aes(fill=Condition),width=0.5)+
        scale_fill_manual(values=c("#2171b5","#B22222","#008B8B"))+  #blue and red
  ylim(4,9)+
    ##geom_point(aes(color=cell_line, shape=Section), size=5)
    geom_point(aes(color=CellLine),size=3)+
    scale_color_viridis(option = "D", discrete=TRUE)+
    geom_point(shape = 1,size = 3,colour = "black")+
   #facet_grid(fct_relevel(Day, "15", "60"), scales="free") +
     labs(x     ="",
        y     = "Relative GCase activity",
       fill  = "Condition",
      title = "GCase activity") +
    #stat_compare_means(comparisons=list(c("Control", "PD N370S")), method = "t.test", label="p.signif", label.x = 1.5)+
    stat_compare_means(comparisons=list(c("Control", "PD-N370S")), method = "wilcox.test", label="p.signif", label.x = 1.5, size=7)+

    #ggpubr::stat_compare_means(comparisons=my_comparisons, method="wilcox.test", p.adjust.method="BH",label="p.signif", label.x = 1.5)+
    theme_bw() +
       theme(plot.title=element_text(size=20, face="bold", hjust=0.5),
          axis.text.x=element_text(size = 17, face = "bold",angle=45, hjust=1),
          axis.text.y=element_text(size = 16),
          axis.title.x=element_blank(), 
          axis.title.y=element_text(size=20),
          legend.title = element_text(size=20),
          legend.text = element_text(size=16))-> p #change legend text font size)  
  t<- cowplot::ggdraw(cowplot::add_sub(p, "wilcox.test, ***p=0.001, **p=0.01, *p=0.05",hjust=-0.1, size=15))

  print(t)
ggsave(paste0(Sys.Date()," GCase activity DIV30.pdf"), plot=t, height=7, width=6*1.2, path=OutPath)
```


