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Heatmap.R
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Heatmap.R
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#Filters on the visit numbers you selected. If you choose within subject ANOVA, it also only keeps participants that has all the selected visits.
Heatmap_filterData <- reactive({
#Apply all Filters
dataset <- selectedDataset_Clinical()
dataset <- dataset %>% filter(event_date >= input$Heatmap_eventDate[1] & event_date <= input$Heatmap_eventDate[2])
dataset <- dataset %>% filter(event_type %in% input$Heatmap_EventType)
dataset <- dataset %>% filter(EndpointDay0 %in% input$Heatmap_Endpoint0)
dataset <- dataset %>% filter(EndpointDay14 %in% input$Heatmap_Endpoint14)
dataset <- dataset %>% filter(sex %in% input$Heatmap_Sex)
dataset <- dataset %>% filter(death %in% input$Heatmap_death)
dataset <- dataset %>% filter(admit_age >= input$Heatmap_Age[1] & admit_age <= input$Heatmap_Age[2])
return(dataset)
})
observeEvent(StatResults(), #Observe changes in the reactive DF.
updatePickerInput(session = session, inputId = "Heatmap_Protein",
choices = StatResults()$Protein,
selected = (StatResults() %>% filter(StatResults()$Padjusted < 0.05))$Protein)
)
output$SelectedSamples <- renderPlot({
Heatmap_filterData() %>% tally() %>%
ggplot(aes(1, y = n))+
geom_bar(stat = "identity") +
theme_light() +
geom_text(aes(label= n), position=position_dodge(width=0.9), vjust=-0.25) +
theme(legend.position = "none") +
ggtitle("Number of Samples Selected") +
xlab("") + ylab("") +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())
})
#Plot the heatmap.
output$HeatMapSuper <- DT::renderDataTable({
if (input$Heatmap_Grouping ==T){
#copy data
grouped_data <- Heatmap_filterData()
grouped_data[,22:ncol(grouped_data)] <- scale(grouped_data[,22:ncol(grouped_data)])
GroupedData <- grouped_data %>% dplyr::group_by(across(all_of(input$Heatmap_groupby))) %>%
summarise(across(where(is.numeric), ~ mean(.x, na.rm = TRUE))) %>% ungroup()
clinical <- t(as.matrix(Heatmap_filterData() %>% dplyr::select(colnames(clinical_data))))
proteins <- t(as.matrix(scale(Heatmap_filterData() %>% dplyr::select(!!!input$Heatmap_Protein))))
return(proteins)
}
else if (input$Heatmap_Grouping == F){
Heatmap_filterData()
}
})
output$HMT <- renderPlot({
if (input$Heatmap_Grouping ==T){
#copy data
grouped_data <- Heatmap_filterData()
#Select only numeric data and scale it
grouped_data[,22:ncol(grouped_data)] <- scale(grouped_data[,22:ncol(grouped_data)])
#Group based on input. Calculate mean. Ungroup
GroupedData <- grouped_data %>% dplyr::group_by(across(all_of(input$Heatmap_groupby))) %>%
summarise(across(where(is.numeric), ~ mean(.x, na.rm = TRUE))) %>% ungroup()
#Split the two. We select proteins based on input and the group_by factors --> these should be of interest here
clinical <- t(as.matrix(GroupedData %>% dplyr::select(!!!input$Heatmap_groupby)))
proteins <- t(as.matrix(GroupedData %>% dplyr::select(!!!input$Heatmap_Protein)))
#how to make an x amount of Annotations?!
#Try to make each of them.
#Make a list with all of them and if not an item --> NULL.
Annot_EndpointDay0 <- try(HeatmapAnnotation(EndpointDay0 = clinical["EndpointDay0",],
col = list(EndpointDay0 = c("Discharged(1-2)" = "#A5D6A7",
"Hospitalized(3-4)" = "#FFF59D",
"Hospitalized(5-6)" = "#FFAB91"))))
Annot_EndpointDay14 <- try(HeatmapAnnotation(EndpointDay14 = clinical["EndpointDay14",],
col = list(EndpointDay14 = c("Discharged(1-2)" = "#4CAF50",
"Hospitalized(3-4)" = "#FFEB3B",
"Hospitalized(5-6)" = "#FF5722",
"Dead(7)" = "#424242"))))
Annot_sex <- try(HeatmapAnnotation(sex = clinical["sex",],
col = list(sex = c("Female" = "#DC2543", "Male" = "#1E94A0"))))
Annot_death <- try(HeatmapAnnotation(death = clinical["death",],
col = list(death = c("TRUE" = "black", "FALSE" = "white"))))
Annot_eventType <- try(HeatmapAnnotation(VisitNumber = clinical["event_type",]))
#Make empty list
AnnotList <- HeatmapAnnotation(foo = anno_empty(border = TRUE))
#Repeat this 5 times
if (exists("Annot_EndpointDay0")){
try(AnnotList <- c(AnnotList, Annot_EndpointDay0))
}
if (exists("Annot_EndpointDay14")){
try(AnnotList <- c(AnnotList, Annot_EndpointDay14))
}
if (exists("Annot_sex")){
try(AnnotList <- c(AnnotList, Annot_sex))
}
if (exists("Annot_death")){
try(AnnotList <- c(AnnotList, Annot_death))
}
if (exists("Annot_eventType")){
try(AnnotList <- c(AnnotList, Annot_eventType))
}
#Add thingy
heatmapPlot <- Heatmap(proteins, top_annotation = AnnotList)
draw(heatmapPlot)
}
else if (input$Heatmap_Grouping == F){
clinical <- t(as.matrix(Heatmap_filterData() %>% dplyr::select(colnames(clinical_data))))
proteins <- t(as.matrix(scale(Heatmap_filterData() %>% dplyr::select(!!!input$Heatmap_Protein))))
Annot_EndpointDay0 <- HeatmapAnnotation(EndpointDay0 = clinical["EndpointDay0",],
col = list(EndpointDay0 = c("Discharged(1-2)" = "#A5D6A7",
"Hospitalized(3-4)" = "#FFF59D",
"Hospitalized(5-6)" = "#FFAB91")))
Annot_EndpointDay14 <- HeatmapAnnotation(EndpointDay14 = clinical["EndpointDay14",],
col = list(EndpointDay14 = c("Discharged(1-2)" = "#4CAF50",
"Hospitalized(3-4)" = "#FFEB3B",
"Hospitalized(5-6)" = "#FF5722",
"Dead(7)" = "#424242")))
Annot_sex <- HeatmapAnnotation(sex = clinical["sex",], col = list(sex = c("Female" = "#DC2543", "Male" = "#1E94A0")))
Annot_death <- HeatmapAnnotation(death = clinical["death",],
col = list(death = c("TRUE" = "black", "FALSE" = "white")))
Annot_eventType <- HeatmapAnnotation(VisitNumber = clinical["event_type",])
Annot_Age <- HeatmapAnnotation(age = anno_points(as.numeric(clinical["admit_age",])))
Annotlist <- c(Annot_death, Annot_Age, Annot_sex, Annot_eventType, Annot_EndpointDay0, Annot_EndpointDay14)
heatmapPlot <- Heatmap(proteins, top_annotation = Annotlist)
draw(heatmapPlot)
}
})