library(ggplot2)
library(pheatmap)
library(RColorBrewer)
library(tidyverse)
library(ggpubr)
library(ggsignif)
library(openxlsx)
library(viridis)
package 㤼㸱viridis㤼㸲 was built under R version 4.0.5Loading required package: viridisLite
package 㤼㸱viridisLite㤼㸲 was built under R version 4.0.5
library(dplyr)
library(jcolors)
library(stringr)
setwd("//atlas.uni.lux/users/isabel.rosety/GBA/protein/WB/Plotting with Rstudio")
The working directory was changed to //atlas.uni.lux/users/isabel.rosety/GBA/protein/WB/Plotting with Rstudio 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.xlsx("WB data for Rstudio plots.xlsx", sheet = 2)
data15 <- read.xlsx("WB data for Rstudio plots.xlsx", sheet = 1) #15d are in sheet 1
data30 <- read.xlsx("WB data for Rstudio plots.xlsx", sheet = 2)
data60 <- read.xlsx("WB data for Rstudio plots.xlsx", sheet = 3) #60d are in sheet
data90 <-read.xlsx("WB data for Rstudio plots.xlsx", sheet = 4)
dataCombined <- bind_rows(data15, data60,data30,data90)
dataCombined %>%
mutate(Condition = str_replace_all(Condition,
pattern = "PD N370S", replacement = "GBA-PD")) %>%
mutate(Condition = str_replace_all(Condition,
pattern = "Control", replacement = "CTRL")) ->dataCombined
Normalizing to the mean of the controls per feature
#Normalizing to mean of controls per WB
feature_names <- colnames(dataCombined[,6:ncol(dataCombined)])
dataCombined%>%
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=="CTRL") %>%
group_by(WB,Features,Day, Condition) %>%
#group_by(Features, Batch) %>%
summarise(baseline = mean(Measure)) %>%
ungroup() %>%
dplyr::select(-Condition) %>%
#dplyr::select(-Batch) %>%
full_join(Table_all_long2, by=c("Features","WB","Day")) %>%
#full_join(Table_all_long2, by=c("Features")) %>% #if I wanted to normalize per WB membrane
mutate(Foldchange = Measure /baseline) ->Table_all_based
`summarise()` has grouped output by 'WB', 'Features', 'Day'. You can override using the `.groups` argument.
#Mean of replicates if samples from same batch have been probed for the same marker in different membranes
Table_all_based %>%
group_by(Features,Day, Condition, Batch,CellLine) %>%
summarize(MeanFeatures = mean(Foldchange)) -> Table_Mean
`summarise()` has grouped output by 'Features', 'Day', 'Condition', 'Batch'. You can override using the `.groups` argument.
included_vars =c("Batch", "CellLine", "Condition","Day")
Table_Mean %>%
pivot_wider(all_of(included_vars),names_from = Features,values_from = MeanFeatures)->Table_all_based_wide
#write.csv(Table_all_based_wide, file = 'Table_all_Mean of replicates and Normalized to mean of controls.csv')
Table_all_based_wide15<-filter(Table_all_based_wide,Day=="D15")
Table_all_based_wide30<-filter(Table_all_based_wide,Day=="D30")
Table_all_based_wide60<-filter(Table_all_based_wide,Day=="D60")
Table_all_based_wide90<-filter(Table_all_based_wide,Day=="D90")
#Table of normalized values without doing the mean of replicates
included_vars2 =c("WB","Batch", "CellLine", "Condition","Day")
Table_all_based%>%
pivot_wider(all_of(included_vars2),names_from = Features,values_from = Foldchange)->Table_all_normalized_no_mean
#write.csv(Table_all_normalized_no_mean, file = 'Table_all_Normalized to mean of controls NO mean of replicates.csv')
Plots for all features one timepoint (loop)
feature_names <- colnames(Table_all_based_wide30[,5:ncol(Table_all_based_wide30)])
#subset = c("CellLine","Condition","Day","Batch","p62ByActin")
#Table_all_based_wide%>% #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(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=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=4)
}


















Plots for all features and all timepoints (loop)
for (i in 1:length(feature_names)) {
Table_all_based_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)+ #show.legend will remove box around the points of the legend
geom_point(aes(color=CellLine),size=2,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])) +
theme_bw() +
theme(
axis.line = element_line(colour = 'black', size = 0.5) ,
axis.title.x = element_blank(),
axis.text.x = element_text(size=14, color="black", angle=45, vjust = 1, hjust = 0.95),
axis.title.y = element_text(size = 20),
axis.text.y = element_text(size=15, color="black"),
axis.ticks.y = element_line(),
axis.ticks.length=unit(.25, "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=14, 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)
}


















---
title: "R Notebook"
output: html_notebook
---
---
```{r}
library(ggplot2)
library(pheatmap)
library(RColorBrewer)
library(tidyverse)
library(ggpubr)
library(ggsignif)
library(openxlsx)
library(viridis)
library(dplyr)
library(jcolors)
library(stringr)
```

```{r}
setwd("//atlas.uni.lux/users/isabel.rosety/GBA/protein/WB/Plotting with Rstudio")

data <- read.xlsx("WB data for Rstudio plots.xlsx", sheet = 2)
data15 <- read.xlsx("WB data for Rstudio plots.xlsx", sheet = 1) #15d are in sheet 1
data30 <- read.xlsx("WB data for Rstudio plots.xlsx", sheet = 2)
data60 <- read.xlsx("WB data for Rstudio plots.xlsx", sheet = 3) #60d are in sheet 
data90 <-read.xlsx("WB data for Rstudio plots.xlsx", sheet = 4)
dataCombined <- bind_rows(data15, data60,data30,data90)
dataCombined %>% 
   mutate(Condition = str_replace_all(Condition, 
            pattern = "PD N370S", replacement = "GBA-PD")) %>%
     mutate(Condition = str_replace_all(Condition, 
            pattern = "Control", replacement = "CTRL")) ->dataCombined

```


Normalizing to the mean of the controls per feature
```{r} 

#Normalizing to mean of controls per WB
feature_names <- colnames(dataCombined[,6:ncol(dataCombined)])
  dataCombined%>%
    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=="CTRL") %>% 
    group_by(WB,Features,Day, Condition) %>% 
    #group_by(Features, Batch) %>% 
    summarise(baseline = mean(Measure)) %>% 
    ungroup() %>% 
    dplyr::select(-Condition) %>% 
    #dplyr::select(-Batch) %>%
    full_join(Table_all_long2, by=c("Features","WB","Day")) %>% 
    #full_join(Table_all_long2, by=c("Features")) %>% #if I wanted to normalize per WB membrane
    mutate(Foldchange = Measure /baseline) ->Table_all_based

#Mean of replicates if samples from same batch have been probed for the same marker in different membranes
Table_all_based %>%
  group_by(Features,Day, Condition, Batch,CellLine) %>%
  summarize(MeanFeatures = mean(Foldchange)) -> Table_Mean

included_vars =c("Batch", "CellLine", "Condition","Day")
Table_Mean %>% 
    pivot_wider(all_of(included_vars),names_from = Features,values_from = MeanFeatures)->Table_all_based_wide

#write.csv(Table_all_based_wide, file = 'Table_all_Mean of replicates and Normalized to mean of controls.csv')

Table_all_based_wide15<-filter(Table_all_based_wide,Day=="D15")
Table_all_based_wide30<-filter(Table_all_based_wide,Day=="D30")
Table_all_based_wide60<-filter(Table_all_based_wide,Day=="D60")
Table_all_based_wide90<-filter(Table_all_based_wide,Day=="D90")


#Table of normalized values without doing the mean of replicates
included_vars2 =c("WB","Batch", "CellLine", "Condition","Day")
Table_all_based%>% 
    pivot_wider(all_of(included_vars2),names_from = Features,values_from = Foldchange)->Table_all_normalized_no_mean
#write.csv(Table_all_normalized_no_mean, file = 'Table_all_Normalized to mean of controls NO mean of replicates.csv')

```

Plots for all features one timepoint (loop)
```{r} 
feature_names <- colnames(Table_all_based_wide30[,5:ncol(Table_all_based_wide30)])

#subset = c("CellLine","Condition","Day","Batch","p62ByActin")
#Table_all_based_wide%>% #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(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=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=4)
}
```

Plots for all features and all timepoints (loop)
```{r}
for (i in 1:length(feature_names)) {
Table_all_based_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)+ #show.legend will remove box around the points of the legend
    geom_point(aes(color=CellLine),size=2,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])) +
  theme_bw() +
  theme(
    axis.line = element_line(colour = 'black', size = 0.5) ,
    axis.title.x = element_blank(),
    axis.text.x = element_text(size=14, color="black", angle=45, vjust = 1, hjust = 0.95),
    axis.title.y = element_text(size = 20),
    axis.text.y = element_text(size=15, color="black"),
    axis.ticks.y = element_line(),
    axis.ticks.length=unit(.25, "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=14, 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)
}
```


