library(ggplot2)
# library(RColorBrewer)
library(tidyverse)
library(ggpubr)
library(jcolors)

# load data 
# in excel pre generate a file with only including well information / R error when duplicated row names 

setwd("//atlas.uni.lux/users/isabel.rosety/GBA/MEA/RstudioAnalysis/")
The working directory was changed to //atlas.uni.lux/users/isabel.rosety/GBA/MEA/RstudioAnalysis 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.
# NOTE: before and during loading, re-check the index of the rows. it is variable in some files (not fix index between extracted files)


#Day 15
d15_1<- read.csv("//atlas.uni.lux/users/isabel.rosety/GBA/MEA/20210729_MEA_E40/E40D15/Neural_Statistics_Compiler_20210803_E40d15.csv",header = T, skip = 157, sep=";",     nrows = 26)
n <- d15_1$Measurement
d15_1t <- as.data.frame(t(d15_1[,-1])) 
colnames( d15_1t) <- n  
m <- rownames(d15_1t) 
d15_1t %>% mutate(wells = rownames(d15_1t)) %>% mutate(timePoint = "day15") %>% mutate(day = 15) %>% mutate(experiment = "exp40") ->  d15_1t
m -> rownames(d15_1t)

d15_2<- read.csv("//atlas.uni.lux/users/isabel.rosety/GBA/MEA/20210806_MEA_E41/E41D15/20210806_E41_D15(000).csv",header = T, skip = 157, sep=";",     nrows = 26)
n <- d15_2$Measurement
d15_2t <- as.data.frame(t(d15_2[,-1])) 
colnames( d15_2t) <- n  
m <- rownames(d15_2t) 
d15_2t %>% mutate(wells = rownames(d15_2t)) %>% mutate(timePoint = "day15") %>% mutate(day = 15) %>% mutate(experiment = "exp41") ->  d15_2t
m -> rownames(d15_2t)

d15_3<- read.csv("//atlas.uni.lux/users/isabel.rosety/GBA/MEA/20210812_MEA_E42_E43/E42D10/20210812_E42D10(000).csv",header = T, skip = 157, sep=";",     nrows = 26)
n <- d15_3$Measurement
d15_3t <- as.data.frame(t(d15_3[,-1])) 
colnames( d15_3t) <- n  
m <- rownames(d15_3t) 
d15_3t %>% mutate(wells = rownames(d15_3t)) %>% mutate(timePoint = "day15") %>% mutate(day = 15) %>% mutate(experiment = "exp42") ->  d15_3t
m -> rownames(d15_3t)

d15_4<- read.csv("//atlas.uni.lux/users/isabel.rosety/GBA/MEA/20210812_MEA_E42_E43/E43D15/20210820_E43_D15(001).csv",header = T, skip = 157, sep=";",     nrows = 26)
n <- d15_4$Measurement
d15_4t <- as.data.frame(t(d15_4[,-1])) 
colnames( d15_4t) <- n  
m <- rownames(d15_4t) 
d15_3t %>% mutate(wells = rownames(d15_4t)) %>% mutate(timePoint = "day15") %>% mutate(day = 15) %>% mutate(experiment = "exp43") ->  d15_4t
m -> rownames(d15_4t)  
 
 

Merge data. obtain sumary data file (over threshold and without threshold)


# cahnge folder for results 
setwd("//atlas.uni.lux/users/isabel.rosety/GBA/MEA/RstudioAnalysis/")
The working directory was changed to //atlas.uni.lux/users/isabel.rosety/GBA/MEA/RstudioAnalysis 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.
 ## merge all files form different time points 
dataWell <- rbind(d15_1t,d15_2t,d15_3t,d15_4t)

#dataWell <- rbind(d15_1t,d15_2t,d15_3t,d15_4t)
#dataWell[dataWell==0] <- NA
head(dataWell)

# add conditions to wells 


colnames(dataWell)<-c( 'NumberOfSpikes', 'MeanFiringRate', 'ISI', 'NumberOfBurst','BurstDuration_avrg', 'BurstDuration_std', 'NumberSpikesPerBurst_avg', 'NumberSpikesPerBurst_std', 'meanISI_burst_avg','meanISI_burst_std','medianISI_burst_avg','medianISI_burst_std','InterBurstInterval_avg', 'InterBurstInterval_std', 'BurstFrequency', 'IBI_CoefficientOfVariation', 'Normalized_Duration_IQR','BurstPercentage', 'condition','CellLine','wells', 'timepoint', 'day','experiment')

# create colum for removal of outliers (per condition & timepoint)
dataWell %>% mutate(out = paste(dataWell$condition, dataWell$timepoint)) ->  dataWell #here we create a new column called out, with condition and timepoint, because outliers will be calculated based on condition and timepoint

# convert into numeric all data of interest fro plotting (needed because data was as character) 
dataWell$NumberOfSpikes <- as.numeric(as.character(dataWell$NumberOfSpikes))
dataWell$MeanFiringRate <- as.numeric(as.character(dataWell$MeanFiringRate))
dataWell$ISI <- as.numeric(as.character(dataWell$ISI))
dataWell$NumberOfBurst <- as.numeric(as.character(dataWell$NumberOfBurst))
dataWell$BurstDuration_avrg <- as.numeric(as.character(dataWell$BurstDuration_avrg))
dataWell$BurstDuration_std <- as.numeric(as.character(dataWell$BurstDuration_std))
dataWell$NumberSpikesPerBurst_avg <- as.numeric(as.character(dataWell$NumberSpikesPerBurst_avg))
dataWell$NumberSpikesPerBurst_std <- as.numeric(as.character(dataWell$NumberSpikesPerBurst_std))
dataWell$meanISI_burst_avg <- as.numeric(as.character(dataWell$meanISI_burst_avg))
dataWell$meanISI_burst_std <- as.numeric(as.character(dataWell$meanISI_burst_std))
dataWell$medianISI_burst_std <- as.numeric(as.character(dataWell$medianISI_burst_std))
dataWell$medianISI_burst_avg <- as.numeric(as.character(dataWell$medianISI_burst_avg))
dataWell$InterBurstInterval_avg <- as.numeric(as.character(dataWell$InterBurstInterval_avg))
dataWell$InterBurstInterval_std <- as.numeric(as.character(dataWell$InterBurstInterval_std))
dataWell$BurstFrequency <- as.numeric(as.character(dataWell$BurstFrequency))
dataWell$IBI_CoefficientOfVariation <- as.numeric(as.character(dataWell$IBI_CoefficientOfVariation))
dataWell$Normalized_Duration_IQR <- as.numeric(as.character(dataWell$Normalized_Duration_IQR))
dataWell$BurstPercentage <- as.numeric(as.character(dataWell$BurstPercentage))
dataWell$day <- as.numeric(as.character(dataWell$day))


# obtain data only with bursting information for extracting into CSV and plotting in graphad (needed to take into consideration SD already calculated by software)
dataWell_burst <- dataWell[dataWell$NumberOfBurst != 0, ] #remove wells with no information for bursting 

dataWell_5spike <- dataWell[dataWell$NumberOfSpikes >= 5, ] #this is to select the measurements with more than 5 spikes per minute
dataWell_5spike_burst <- dataWell_burst[dataWell_burst$NumberOfSpikes >= 5, ]
dataWell_5spike<-filter(dataWell_5spike, condition %in% c("Control","PD_N370S"))

feature_names <- colnames(dataWell_5spike[,c(1:4,15:18)])  
#saving info: 

  #save summary file in CSV
  write.csv(dataWell,file="dataWell_allInfo.csv")
  write.csv(dataWell_5spike,file="dataWell_5spike.csv")
  # save information reagrding average and std
  write.csv(dataWell_burst,file="dataWell_burst_allInfo.csv") 
  write.csv(dataWell_5spike_burst,file="dataWell_5spike_burst.csv")

Remove outliers based on 25th & 75th percentiles (for only the specific feature under study_ introducetion NAs) thes rest of the information for the full well remains

# NOTE!! if zeros are kept, may infomation will be extracted based on a simple value due to the ceros being the mean !! 
# transfrom 0 into NAs 

#in this one it wont remove the entire electrode, it will only remove the value of the outlier feature

dataWell_noZero <- replace(dataWell_5spike, dataWell_5spike == 0, NA)
#dataWell_noZero <- replace(dataWell, dataWell == 0, NA)

 groupOut <- unique(dataWell_noZero$out) # groupout 
datatest_NA <- data.frame()


for (i in 1:length(groupOut))
{
  datatest <- dataWell_noZero %>% 
  filter(out %in% groupOut[i]) # go for each line and extract rows for that line 
  for (j in 1:18) # for (j in 2:length(exprs))
  {
    out <- boxplot.stats(datatest[,j])$out
    for (k in 1:length(out))
    {
    datatest[,j][datatest[,j] == out[k]] <- NA #it changes the outlier value for NA
    }
    rm(out)
  }
  datatest_NA <- rbind(datatest_NA,datatest)
  rm(datatest)
} 


#saving info: 
# files without outliers were generated from file No zeros - reason: too many zeros cause exclusion of real values as outliers 
# _well > meaningn all well information was removed for all variable when at least one of the variables presented one outlier -> the well is not consider any longer 

# Remove an entire row if it has >22 NAs (for example >50% of your features)
count_na <- function(x)sum(is.na(x))
data_noOut_value <- datatest_NA %>%
  dplyr::mutate(count_na = apply(., 1, count_na))
data_noOut_value <-datatest_NA[!(data_noOut_value$count_na >= 19),] # remove wells containing no info 

dataWell_burst_NoOut_value <- subset(data_noOut_value, !is.na(NumberOfBurst)) #remove wells with no information for bursting

  write.csv(dataWell_burst_NoOut_value,file="dataWell_burst_NoOut_value.csv") # infor for bursting (graphad) _ only the specific outlier was removed. well information for other variables remain  
  write.csv(data_noOut_value,file="data_noOut_value.csv") # infor - no outliers for specific variable -well info remains 
  


#dataWell2=dataWell[!grepl("exp40",dataWell$experiment),]
feature_names <- colnames(data_noOut_value[,c(1:4,15:18)])  
head(data_noOut_value)
NA

Shapiro Test to check normality # plotting without SD already calculated (“NumberOfSpikes” “MeanFiringRate” “ISI” “NumberOfBurst” “NumberBurstingElectrodes”)

#if p-value >0.05, normality can be assumed
TestShapiro<- select(data_noOut_value, c(1:18))
#shapiro.test(data_noOut_value$MeanFiringRate)
apply(TestShapiro,2,shapiro.test)

Plots


names <- c("Number Of Spikes", "Mean Firing Rate (spikes/s)" , "Interspike Interval [ISI]"  ,"Number Of Bursts" , "Burst Frequency (bursts/s)","IBI Coefficient Of Variation", "Normalized Duration IQR"  ,  "Burst Percentage")

for (i in 1:length(feature_names)) {
data_noOut_value  %>% #change dataset selected - change saving option depending on the dataset 
  pivot_longer(cols=feature_names, names_to = "feature", values_to = "value") %>%
  filter(feature %in% feature_names[i]) %>%
  #ggplot( aes(x = factor(condition, level = c( 'Control','PD_N370S', 'Control_GDNF','PD_N370S_GDNF')), y=value) ) +
  #geom_boxplot(aes(fill=fct_relevel(condition, 'Control','PD_N370S', 'Control_GDNF','PD_N370S_GDNF')),width=0.5,outlier.shape=NA) +
  ggplot( aes(x = factor(condition, level = c( 'Control','PD_N370S')), y=value) ) +
  geom_boxplot(aes(fill=fct_relevel(condition, 'Control','PD_N370S')),width=0.8,outlier.shape=NA) +
    geom_point(aes(color=CellLine),size=3,show.legend = T,alpha = 0.5)+
   
    scale_color_jcolors("pal7")+
    theme(legend.key=element_blank()) +
  #scale_fill_manual(values= c("#008B8B","#B22222","#2171b5","#000066"),name = "condition", guide = "none") +
    scale_fill_manual(values= c("white", "black"),name = "condition", guide = "none") +  
  #stat_compare_means(comparisons=list(c("WT", "3xSNCA")), method = "t.test", label="p.signif", label.x = 1.5)+
  
  # Mann-Whitney
   geom_signif(comparisons = list(c('Control','PD_N370S')), 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(timepoint, "day10", "day15","day30", "day60","day100"), scales="free")  +
    

  labs(x     ="",
       y     = paste(names[i]),
       fill  = "Condition",
       title = paste(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=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=-0.5, size=15))
  print(t)

#ggsave(paste0(Sys.Date(),"_", feature_names[i], "_value_electrode_DIV15.pdf"), plot=t,height=4) # change saving option depending on the dataset 

}

Per cell line


for (i in 1:length(feature_names)) {
dataWell_5spike %>% #change dataset selected - change saving option depending on the dataset 
  pivot_longer(cols=feature_names, names_to = "feature", values_to = "value") %>%
  filter(feature %in% feature_names[i]) %>%
  #ggplot( aes(x = factor(condition, level = c( 'Control','PD_N370S', 'Control_GDNF','PD_N370S_GDNF')), y=value) ) +
  #geom_boxplot(aes(fill=fct_relevel(condition, 'Control','PD_N370S', 'Control_GDNF','PD_N370S_GDNF')),width=0.5,outlier.shape=NA) +
  ggplot( aes(x = factor(CellLine, level = c( 'WT 56','WT 39', 'WT 68', 'MUT 309', 'MUT KTI6', 'MUT SGO1')), y=value) ) +
  geom_boxplot(aes(fill=CellLine),width=0.8,outlier.shape=NA) +
 # scale_fill_manual(values= c("#008B8B","#B22222","#2171b5","#000066"),name = "condition") +
    geom_point(aes(color=experiment),size=2)+
    #scale_color_viridis(option = "D", discrete=TRUE)+
    geom_point(shape = 1,size = 2,colour = "black")+
   # t-test
  #stat_compare_means(comparisons=list(c("WT", "3xSNCA")), method = "t.test", label="p.signif", label.x = 1.5)+
  
  # Mann-Whitney
  #ggpubr::stat_compare_means(comparisons=list(c("Control", "PD_N370S")), method="wilcox.test", p.adjust.method="BH",label="p.signif", label.x = 1.5)+
  
    
  labs(x     ="",
       y     = paste(names[i]),
       fill  = "CellLine",
       title = paste(names[i])) +
  theme_bw() +
    
  theme(
    axis.line = element_line(colour = 'black', size = 0.1) ,
    axis.title.x = element_blank(),
    axis.text.x = element_text(angle=45,size = 7,hjust=1), #angle=45),
    axis.title.y = element_text(size = 18),
    axis.text.y = element_text(size=15, color="black"),
    axis.ticks.y = element_line(),
    legend.position="right",
    legend.text = element_text(size = 11, face = "bold") ,
    legend.title = element_blank(),
    legend.key.size = unit(0.7, "cm"),
    legend.key.width = unit(0.6,"cm"),
    plot.title = element_text(size = 15, 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")
  )  -> p
  #t<- cowplot::ggdraw(cowplot::add_sub(p, "Wilcox-test, ***p=0.001, **p=0.01, *p=0.05",hjust=-0.5, size=9))
  print(p)

#ggsave(paste0(Sys.Date(),"_", feature_names[i], "_value_electrode_NoOutliers.pdf"), plot=t) # change saving option depending on the dataset 

}

One feature


dataWellOneF <-filter(dataWellNoNA,timepoint=="day15")
dataWellOneF%>%
  ggplot(aes(x=day, y=MeanFiringRate, group=condition, color=condition)) +
  geom_violin(method='loess', formula ='y ~ x', size = 1)+
  scale_colour_manual(name="Condition", values= c("Control"= "#008B8B", "PD_N370S"="#B22222")) +
  # facet_wrap(~timepoint, scales="free", labeller = labeller(groupwrap = label_wrap_gen(10))) +
  # facet_grid(~fct_relevel(timepoint, "day15","day30",  "day70", "day90"), scales="free") +
  labs(x="Time of differentiation [days]",
        y     = "Mean Firing Rate",
       fill  = "Condition",
       title = "Mean Firing Rate") +
   theme_bw() -> p
  t<- cowplot::ggdraw(cowplot::add_sub(p, "Locally estimated scatterplot smoothing: y ~ Time",hjust=-0.2, size=9))
  print(t)
  ggsave(paste0(Sys.Date(),"MeanFiringRate_timeline_data_noOut_value.pdf"), plot=t)
---
title: "nalysis of MEA data and plotting - using output from matlab"
output: html_notebook
---


```{r}
library(ggplot2)
# library(RColorBrewer)
library(tidyverse)
library(ggpubr)
library(jcolors)
```


```{r}

# load data 
# in excel pre generate a file with only including well information / R error when duplicated row names 

setwd("//atlas.uni.lux/users/isabel.rosety/GBA/MEA/RstudioAnalysis/")


# NOTE: before and during loading, re-check the index of the rows. it is variable in some files (not fix index between extracted files)


#Day 15
d15_1<- read.csv("//atlas.uni.lux/users/isabel.rosety/GBA/MEA/20210729_MEA_E40/E40D15/Neural_Statistics_Compiler_20210803_E40d15.csv",header = T, skip = 157, sep=";",     nrows = 26)
n <- d15_1$Measurement
d15_1t <- as.data.frame(t(d15_1[,-1])) 
colnames( d15_1t) <- n  
m <- rownames(d15_1t) 
d15_1t %>% mutate(wells = rownames(d15_1t)) %>% mutate(timePoint = "day15") %>% mutate(day = 15) %>% mutate(experiment = "exp40") ->  d15_1t
m -> rownames(d15_1t)

d15_2<- read.csv("//atlas.uni.lux/users/isabel.rosety/GBA/MEA/20210806_MEA_E41/E41D15/20210806_E41_D15(000).csv",header = T, skip = 157, sep=";",     nrows = 26)
n <- d15_2$Measurement
d15_2t <- as.data.frame(t(d15_2[,-1])) 
colnames( d15_2t) <- n  
m <- rownames(d15_2t) 
d15_2t %>% mutate(wells = rownames(d15_2t)) %>% mutate(timePoint = "day15") %>% mutate(day = 15) %>% mutate(experiment = "exp41") ->  d15_2t
m -> rownames(d15_2t)

d15_3<- read.csv("//atlas.uni.lux/users/isabel.rosety/GBA/MEA/20210812_MEA_E42_E43/E42D10/20210812_E42D10(000).csv",header = T, skip = 157, sep=";",     nrows = 26)
n <- d15_3$Measurement
d15_3t <- as.data.frame(t(d15_3[,-1])) 
colnames( d15_3t) <- n  
m <- rownames(d15_3t) 
d15_3t %>% mutate(wells = rownames(d15_3t)) %>% mutate(timePoint = "day15") %>% mutate(day = 15) %>% mutate(experiment = "exp42") ->  d15_3t
m -> rownames(d15_3t)

d15_4<- read.csv("//atlas.uni.lux/users/isabel.rosety/GBA/MEA/20210812_MEA_E42_E43/E43D15/20210820_E43_D15(001).csv",header = T, skip = 157, sep=";",     nrows = 26)
n <- d15_4$Measurement
d15_4t <- as.data.frame(t(d15_4[,-1])) 
colnames( d15_4t) <- n  
m <- rownames(d15_4t) 
d15_3t %>% mutate(wells = rownames(d15_4t)) %>% mutate(timePoint = "day15") %>% mutate(day = 15) %>% mutate(experiment = "exp43") ->  d15_4t
m -> rownames(d15_4t)  
 
 
```

Merge data. obtain sumary data file (over threshold and without threshold) 
```{r}

# cahnge folder for results 
setwd("//atlas.uni.lux/users/isabel.rosety/GBA/MEA/RstudioAnalysis/")

 ## merge all files form different time points 
dataWell <- rbind(d15_1t,d15_2t,d15_3t,d15_4t)

#dataWell <- rbind(d15_1t,d15_2t,d15_3t,d15_4t)
#dataWell[dataWell==0] <- NA
head(dataWell)

# add conditions to wells 


colnames(dataWell)<-c( 'NumberOfSpikes', 'MeanFiringRate', 'ISI', 'NumberOfBurst','BurstDuration_avrg', 'BurstDuration_std', 'NumberSpikesPerBurst_avg', 'NumberSpikesPerBurst_std', 'meanISI_burst_avg','meanISI_burst_std','medianISI_burst_avg','medianISI_burst_std','InterBurstInterval_avg', 'InterBurstInterval_std', 'BurstFrequency', 'IBI_CoefficientOfVariation', 'Normalized_Duration_IQR','BurstPercentage', 'condition','CellLine','wells', 'timepoint', 'day','experiment')

# create colum for removal of outliers (per condition & timepoint)
dataWell %>% mutate(out = paste(dataWell$condition, dataWell$timepoint)) ->  dataWell #here we create a new column called out, with condition and timepoint, because outliers will be calculated based on condition and timepoint

# convert into numeric all data of interest fro plotting (needed because data was as character) 
dataWell$NumberOfSpikes <- as.numeric(as.character(dataWell$NumberOfSpikes))
dataWell$MeanFiringRate <- as.numeric(as.character(dataWell$MeanFiringRate))
dataWell$ISI <- as.numeric(as.character(dataWell$ISI))
dataWell$NumberOfBurst <- as.numeric(as.character(dataWell$NumberOfBurst))
dataWell$BurstDuration_avrg <- as.numeric(as.character(dataWell$BurstDuration_avrg))
dataWell$BurstDuration_std <- as.numeric(as.character(dataWell$BurstDuration_std))
dataWell$NumberSpikesPerBurst_avg <- as.numeric(as.character(dataWell$NumberSpikesPerBurst_avg))
dataWell$NumberSpikesPerBurst_std <- as.numeric(as.character(dataWell$NumberSpikesPerBurst_std))
dataWell$meanISI_burst_avg <- as.numeric(as.character(dataWell$meanISI_burst_avg))
dataWell$meanISI_burst_std <- as.numeric(as.character(dataWell$meanISI_burst_std))
dataWell$medianISI_burst_std <- as.numeric(as.character(dataWell$medianISI_burst_std))
dataWell$medianISI_burst_avg <- as.numeric(as.character(dataWell$medianISI_burst_avg))
dataWell$InterBurstInterval_avg <- as.numeric(as.character(dataWell$InterBurstInterval_avg))
dataWell$InterBurstInterval_std <- as.numeric(as.character(dataWell$InterBurstInterval_std))
dataWell$BurstFrequency <- as.numeric(as.character(dataWell$BurstFrequency))
dataWell$IBI_CoefficientOfVariation <- as.numeric(as.character(dataWell$IBI_CoefficientOfVariation))
dataWell$Normalized_Duration_IQR <- as.numeric(as.character(dataWell$Normalized_Duration_IQR))
dataWell$BurstPercentage <- as.numeric(as.character(dataWell$BurstPercentage))
dataWell$day <- as.numeric(as.character(dataWell$day))


# obtain data only with bursting information for extracting into CSV and plotting in graphad (needed to take into consideration SD already calculated by software)
dataWell_burst <- dataWell[dataWell$NumberOfBurst != 0, ] #remove wells with no information for bursting 

dataWell_5spike <- dataWell[dataWell$NumberOfSpikes >= 5, ] #this is to select the measurements with more than 5 spikes per minute
dataWell_5spike_burst <- dataWell_burst[dataWell_burst$NumberOfSpikes >= 5, ]
dataWell_5spike<-filter(dataWell_5spike, condition %in% c("Control","PD_N370S"))

feature_names <- colnames(dataWell_5spike[,c(1:4,15:18)])  
#saving info: 

  #save summary file in CSV
  write.csv(dataWell,file="dataWell_allInfo.csv")
  write.csv(dataWell_5spike,file="dataWell_5spike.csv")
  # save information reagrding average and std
  write.csv(dataWell_burst,file="dataWell_burst_allInfo.csv") 
  write.csv(dataWell_5spike_burst,file="dataWell_5spike_burst.csv")


```

Remove outliers based on 25th & 75th percentiles (for only the specific feature under study_ introducetion NAs) thes rest of the information for the full well remains 
```{r} 
# NOTE!! if zeros are kept, may infomation will be extracted based on a simple value due to the ceros being the mean !! 
# transfrom 0 into NAs 

#in this one it wont remove the entire electrode, it will only remove the value of the outlier feature

dataWell_noZero <- replace(dataWell_5spike, dataWell_5spike == 0, NA)
#dataWell_noZero <- replace(dataWell, dataWell == 0, NA)

 groupOut <- unique(dataWell_noZero$out) # groupout 
datatest_NA <- data.frame()


for (i in 1:length(groupOut))
{
  datatest <- dataWell_noZero %>% 
  filter(out %in% groupOut[i]) # go for each line and extract rows for that line 
  for (j in 1:18) # for (j in 2:length(exprs))
  {
    out <- boxplot.stats(datatest[,j])$out
    for (k in 1:length(out))
    {
    datatest[,j][datatest[,j] == out[k]] <- NA #it changes the outlier value for NA
    }
    rm(out)
  }
  datatest_NA <- rbind(datatest_NA,datatest)
  rm(datatest)
} 


#saving info: 
# files without outliers were generated from file No zeros - reason: too many zeros cause exclusion of real values as outliers 
# _well > meaningn all well information was removed for all variable when at least one of the variables presented one outlier -> the well is not consider any longer 

# Remove an entire row if it has >22 NAs (for example >50% of your features)
count_na <- function(x)sum(is.na(x))
data_noOut_value <- datatest_NA %>%
  dplyr::mutate(count_na = apply(., 1, count_na))
data_noOut_value <-datatest_NA[!(data_noOut_value$count_na >= 19),] # remove wells containing no info 

dataWell_burst_NoOut_value <- subset(data_noOut_value, !is.na(NumberOfBurst)) #remove wells with no information for bursting

  write.csv(dataWell_burst_NoOut_value,file="dataWell_burst_NoOut_value.csv") # info for bursting (graphad) _ only the specific outlier was removed. well information for other variables remain  
  write.csv(data_noOut_value,file="data_noOut_value.csv") # infor - no outliers for specific variable -well info remains 
  


#dataWell2=dataWell[!grepl("exp40",dataWell$experiment),]
feature_names <- colnames(data_noOut_value[,c(1:4,15:18)])  
head(data_noOut_value)
  
```

Shapiro Test to check normality
# plotting without SD already calculated ("NumberOfSpikes" "MeanFiringRate"  "ISI"  "NumberOfBurst"  "NumberBurstingElectrodes")
```{r}
#if p-value >0.05, normality can be assumed
TestShapiro<- select(data_noOut_value, c(1:18))
#shapiro.test(data_noOut_value$MeanFiringRate)
apply(TestShapiro,2,shapiro.test)

```


Plots
```{r warning=FALSE}

names <- c("Number Of Spikes", "Mean Firing Rate (spikes/s)" , "Interspike Interval [ISI]"  ,"Number Of Bursts" , "Burst Frequency (bursts/s)","IBI Coefficient Of Variation", "Normalized Duration IQR"  ,  "Burst Percentage")

for (i in 1:length(feature_names)) {
data_noOut_value  %>% #change dataset selected - change saving option depending on the dataset 
  pivot_longer(cols=feature_names, names_to = "feature", values_to = "value") %>%
  filter(feature %in% feature_names[i]) %>%
  #ggplot( aes(x = factor(condition, level = c( 'Control','PD_N370S', 'Control_GDNF','PD_N370S_GDNF')), y=value) ) +
  #geom_boxplot(aes(fill=fct_relevel(condition, 'Control','PD_N370S', 'Control_GDNF','PD_N370S_GDNF')),width=0.5,outlier.shape=NA) +
  ggplot( aes(x = factor(condition, level = c( 'Control','PD_N370S')), y=value) ) +
  geom_boxplot(aes(fill=fct_relevel(condition, 'Control','PD_N370S')),width=0.8,outlier.shape=NA) +
    geom_point(aes(color=CellLine),size=3,show.legend = T,alpha = 0.5)+
   
    scale_color_jcolors("pal7")+
    theme(legend.key=element_blank()) +
  #scale_fill_manual(values= c("#008B8B","#B22222","#2171b5","#000066"),name = "condition", guide = "none") +
    scale_fill_manual(values= c("white", "black"),name = "condition", guide = "none") +  
  #stat_compare_means(comparisons=list(c("WT", "3xSNCA")), method = "t.test", label="p.signif", label.x = 1.5)+
  
  # Mann-Whitney
   geom_signif(comparisons = list(c('Control','PD_N370S')), 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(timepoint, "day10", "day15","day30", "day60","day100"), scales="free")  +
    

  labs(x     ="",
       y     = paste(names[i]),
       fill  = "Condition",
       title = paste(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=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=-0.5, size=15))
  print(t)

#ggsave(paste0(Sys.Date(),"_", feature_names[i], "_value_electrode_DIV15.pdf"), plot=t,height=4) # change saving option depending on the dataset 

}

```
Per cell line
```{r}

for (i in 1:length(feature_names)) {
dataWell_5spike %>% #change dataset selected - change saving option depending on the dataset 
  pivot_longer(cols=feature_names, names_to = "feature", values_to = "value") %>%
  filter(feature %in% feature_names[i]) %>%
  #ggplot( aes(x = factor(condition, level = c( 'Control','PD_N370S', 'Control_GDNF','PD_N370S_GDNF')), y=value) ) +
  #geom_boxplot(aes(fill=fct_relevel(condition, 'Control','PD_N370S', 'Control_GDNF','PD_N370S_GDNF')),width=0.5,outlier.shape=NA) +
  ggplot( aes(x = factor(CellLine, level = c( 'WT 56','WT 39', 'WT 68', 'MUT 309', 'MUT KTI6', 'MUT SGO1')), y=value) ) +
  geom_boxplot(aes(fill=CellLine),width=0.8,outlier.shape=NA) +
 # scale_fill_manual(values= c("#008B8B","#B22222","#2171b5","#000066"),name = "condition") +
    geom_point(aes(color=experiment),size=2)+
    #scale_color_viridis(option = "D", discrete=TRUE)+
    geom_point(shape = 1,size = 2,colour = "black")+
   # t-test
  #stat_compare_means(comparisons=list(c("WT", "3xSNCA")), method = "t.test", label="p.signif", label.x = 1.5)+
  
  # Mann-Whitney
  #ggpubr::stat_compare_means(comparisons=list(c("Control", "PD_N370S")), method="wilcox.test", p.adjust.method="BH",label="p.signif", label.x = 1.5)+
  
    
  labs(x     ="",
       y     = paste(names[i]),
       fill  = "CellLine",
       title = paste(names[i])) +
  theme_bw() +
    
  theme(
    axis.line = element_line(colour = 'black', size = 0.1) ,
    axis.title.x = element_blank(),
    axis.text.x = element_text(angle=45,size = 7,hjust=1), #angle=45),
    axis.title.y = element_text(size = 18),
    axis.text.y = element_text(size=15, color="black"),
    axis.ticks.y = element_line(),
    legend.position="right",
    legend.text = element_text(size = 11, face = "bold") ,
    legend.title = element_blank(),
    legend.key.size = unit(0.7, "cm"),
    legend.key.width = unit(0.6,"cm"),
    plot.title = element_text(size = 15, 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")
  )  -> p
  #t<- cowplot::ggdraw(cowplot::add_sub(p, "Wilcox-test, ***p=0.001, **p=0.01, *p=0.05",hjust=-0.5, size=9))
  print(p)

#ggsave(paste0(Sys.Date(),"_", feature_names[i], "_value_electrode_NoOutliers.pdf"), plot=t) # change saving option depending on the dataset 

}
```

One feature
```{r}

dataWellOneF <-filter(dataWellNoNA,timepoint=="day15")
dataWellOneF%>%
  ggplot(aes(x=day, y=MeanFiringRate, group=condition, color=condition)) +
  geom_violin(method='loess', formula ='y ~ x', size = 1)+
  scale_colour_manual(name="Condition", values= c("Control"= "#008B8B", "PD_N370S"="#B22222")) +
  # facet_wrap(~timepoint, scales="free", labeller = labeller(groupwrap = label_wrap_gen(10))) +
  # facet_grid(~fct_relevel(timepoint, "day15","day30",  "day70", "day90"), scales="free") +
  labs(x="Time of differentiation [days]",
        y     = "Mean Firing Rate",
       fill  = "Condition",
       title = "Mean Firing Rate") +
   theme_bw() -> p
  t<- cowplot::ggdraw(cowplot::add_sub(p, "Locally estimated scatterplot smoothing: y ~ Time",hjust=-0.2, size=9))
  print(t)
  ggsave(paste0(Sys.Date(),"MeanFiringRate_timeline_data_noOut_value.pdf"), plot=t)
```
