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SelectDataset.R
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SelectDataset.R
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#### Functions ####
#Function for Normalization
PatrickNormalizer <- function(dataset){
#We assume log2 transformed data
dataset <- 2^dataset
#Calc normalization Factor
NormalisationFactor <- median(rowSums(dataset, na.rm = TRUE))
#make copy
dataset_normalized <- dataset
#run for loop over each over the rows (samples)
for(i in 1:nrow(dataset)) {
#get sample i, apply normalisation factor on it. Change sample i in the normalized df.
dataset_normalized[i,] <- dataset[i,] * (NormalisationFactor / sum(dataset[i,], na.rm = TRUE))
}
#return
return(dataset_normalized)
}
# Function for Plotting CV ####
CV_Plotter <- function(dataframe){ #function for making the plots
#apple CV function
dataframe_results <- data.frame(apply(dataframe, 1, function(x) (sd(x, na.rm = TRUE) / mean(x, na.rm = TRUE)) * 100))
#change colnames. Just to make life easier
colnames(dataframe_results) <- c("Proteins")
#Plot
dataframe_results %>% ggplot(aes(x = rownames(dataframe_results), y = Proteins)) + geom_point() +
theme_light() + theme(axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank())
}
#### Actual Shiny Code ############
#### INPUTS ####
#set up the Dataset
selectedDataset <- reactive({
#Choose the dataset
if (input$DataSelect == "DDA_Data"){
dataset_select <- DDA_Data
}
else if (input$DataSelect == "DIA_Data"){
dataset_select <- DIA_Data
}
else if (input$DataSelect == "Targeted_Data"){
dataset_select <- Targeted_Data
}
#change to DF format
dataset_select <- as.data.frame(dataset_select)
#Set rownames and remove first column
dataset_select_rownames <- dataset_select[,-1]
rownames(dataset_select_rownames) <- dataset_select[,1]
#Based on yes no for sample removal, we remove URS samples
if (input$URSFilter == TRUE){
dataset_select_rownames <- dataset_select_rownames[!grepl("URS", rownames(dataset_select_rownames)),]
}
#Based on yes no for sample removal, we remove EMT samples
if (input$EmoryFilter == TRUE){
dataset_select_rownames <- dataset_select_rownames[!grepl("EDT-PRT", rownames(dataset_select_rownames)),]
}
#Based on yes no for sample removal, we remove COVID19 Negative samples
if (input$NonPositive == TRUE){
try(dataset_select_rownames <- dataset_select_rownames %>% filter(!rownames(dataset_select_rownames) %in% COVID19Negative$sample_id))
}
return(dataset_select_rownames)
})
#Normalisation
selectedDataset_Normalized <- reactive({
if (input$Normalization == 2){
dataset_select_normalized <- PatrickNormalizer(selectedDataset())
}
if (input$Normalization == 3){
dataset_select_normalized <- as.data.frame(NormalyzerDE::performVSNNormalization(as.matrix(2^selectedDataset())))
}
if (input$Normalization == 1){
dataset_select_normalized <- selectedDataset()
}
return(dataset_select_normalized)
})
#Cut off filter
selectedDataset_Filtered <- reactive({
dataset_select_filtered <- selectedDataset_Normalized() %>% purrr::discard(~sum(is.na(.x))/length(.x)* 100 >= (100 -input$CutOffFilter))
return(dataset_select_filtered)
})
#Connect with the clinical Data
selectedDataset_Clinical <- reactive({
#put the rownames back as column again.
selectedDataset_Filtered_rownametocolumn <- selectedDataset_Filtered() %>% rownames_to_column()
#We only want to keep sample & proteomics info on the ones we have of BOTH of. Thus we use inner_join.
dataset_select_clinical <- inner_join(clinical_data, selectedDataset_Filtered_rownametocolumn, by = c("sample_id" = "rowname"))
return(dataset_select_clinical)
})
#### OUTPUTS ####
#Venny Diagram of DDA, DIA, Targeted
output$VennyThreeMethods <- renderPlot({
#Filter based on Input
DDA_Filtered <- DDA_Data %>% purrr::discard(~sum(is.na(.x))/length(.x)* 100 >= (100 -input$CutOffFilter))
DIA_Filtered <- DIA_Data %>% purrr::discard(~sum(is.na(.x))/length(.x)* 100 >= (100 -input$CutOffFilter))
Targeted_Filtered <- Targeted_Data %>% purrr::discard(~sum(is.na(.x))/length(.x)* 100 >= (100 -input$CutOffFilter))
#Keep column names --> proteinID
DDA_Prots <- colnames(DDA_Filtered[,2:ncol(DDA_Filtered)])
DIA_Prots <- colnames(DIA_Filtered[,2:ncol(DIA_Filtered)])
Targeted_Prots <- colnames(Targeted_Filtered[,2:ncol(Targeted_Filtered)])
#List the three
ProteinIDList <- list("DDA" = DDA_Prots, "DIA" = DIA_Prots, "Targeted" = Targeted_Prots)
ProteinIDListLong <- data.frame(list("DDA" = ncol(DDA_Filtered[,2:ncol(DDA_Filtered)]),
"DIA" = ncol(DIA_Filtered[,2:ncol(DIA_Filtered)]),
"Targeted" = ncol(Targeted_Filtered[,2:ncol(Targeted_Filtered)]))) %>%
gather(key = "Method", value = "Protein_Numbers")
barplot <- try(ProteinIDListLong %>% ggplot(aes(x = Method, y = Protein_Numbers)) +
geom_bar(stat = "identity") +
theme_light())
#Plot
venny <- plot(euler(ProteinIDList, shape = "ellipse"), quantities = TRUE)
ggarrange(barplot, venny, ncol = 2, nrow = 1)
})
output$DataSelection = renderText({
print(paste("There is",
dim(selectedDataset_Filtered())[1],
"Samples Selected and",
dim(selectedDataset_Filtered())[2],
"Protein Selected."))
})
output$DataSelectionPlot <- renderPlot({
items <- c("Samples", "Proteins")
numbers <- c(dim(selectedDataset_Filtered())[1], dim(selectedDataset_Filtered())[2])
DF <- data.frame(items, numbers)
DF %>% ggplot(aes(x = items, y = numbers, fill = items)) +
geom_bar(stat ="identity") +
theme_light() +
geom_text(aes(label= numbers), position=position_dodge(width=0.9), vjust=-0.25) +
theme(legend.position = "none") +
ggtitle("Number of Samples & Proteins") +
xlab("") + ylab("") +
scale_fill_manual(values = c("darkred", "darkblue"))
})
output$SampleBarPlot <- renderPlot({
CV_Plotter(selectedDataset_Filtered())
})