.Rhistory

print(input$text)
print(length(input$text))
#if(length(reactive(as.character(input$text())))<64)
#{
#  sheet_append(ss, data.frame(input$text))
#}
}
attempts(attempts() + 1)
# Commented the below section of code that saves the emial data in aws path
# write.table(input$text, "emails.csv", append = TRUE, row.names = FALSE, col.names = FALSE)
# put_object(
#   file = "./emails.csv",
#   object = "emails.csv",
#   bucket = "sensoryomics-emails1",
#   region = "us-east-2",
#   key = "AKIA6I57NTAM2H7MHLTT",
#   secret = "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"
# )
}
})
updateSelectizeInput(session, 'geneName', choices = gene_names, server = TRUE, selected = gene_names[1], options = list(maxItems = 6, maxOptions = 40))
updateSelectizeInput(session, 'selected_gene', choices = colnames(cleaned_data2[,4:20000]), server = TRUE, selected = col_names[4], options = list(maxItems = 6, maxOptions = 40))
updateSelectizeInput(session, 'Data_Set', choices = dataset_names, server = TRUE, selected = dataset_names[1], options = list(maxItems = 1, maxOptions = 10))
updateSelectizeInput(session, 'dataset', choices = dataset_names, server = TRUE, selected = dataset_names[1], options = list(maxItems = 1, maxOptions = 10))
# Create subsetted reactive list
sub_list <- reactive(subset(gene_names, (gene_names %in% input$geneName)))
debounce(sub_list, 5000)
sub_list_NPP <- reactive(subset(columnames, (columnames %in% input$geneName)))
#sub_list_NPP <- reactive(subset(columnames, (columnames %in% input$selected_gene)))
debounce(sub_list_NPP, 5000)
observe({
#shinyjs::toggle("p1", condition = isTRUE(length(sub_list_NPP())>0))
shinyjs::toggle("p2", condition = isTRUE(length(sub_list_NPP())>1))
shinyjs::toggle("p3", condition = isTRUE(length(sub_list_NPP())>2))
shinyjs::toggle("p4", condition = isTRUE(length(sub_list_NPP())>3))
shinyjs::toggle("p5", condition = isTRUE(length(sub_list_NPP())>4))
shinyjs::toggle("p6", condition = isTRUE(length(sub_list_NPP())>5))
})
# Validation message display code
# output$p1 <- renderPlotly({
#   box_plots()
#   })
box_vals <- reactiveValues()
plot_list <- reactiveVal(list())
# Create a list to store the generated plotly plots
plotly_plot_list <- reactiveVal(list())
# Create a list to store ggplot2 plots
ggplot_list <- reactiveVal(list())
output$p1 <- renderPlotly({
validate(
need(input$geneName,HTML("Please Select the Gene!"))
)
selection <- which(input$geneName[1]==colnames(cleaned_data2), arr.ind = TRUE)
bp1 <- graph_gene(selection)
#plotly_plot_list(c(plotly_plot_list(), list(bp1)))
plot_list <- plot_list(list(bp1))
box_vals$plt <- bp1
return(bp1)
})
output$p2 <- renderPlotly({
if (length(input$geneName)<2){
return(NULL)
}else{
selection <- which(input$geneName[2]==colnames(cleaned_data2), arr.ind = TRUE)
bp2 <- graph_gene(selection)
#plotly_plot_list(c(plotly_plot_list(), list(bp2)))
#box_vals$plt <- bp2
return(bp2)
}
})
output$p3 <- renderPlotly({
if (length(input$geneName)<3){
return(NULL)
}else{
selection <- which(input$geneName[3]==colnames(cleaned_data2), arr.ind = TRUE)
bp3 <- graph_gene(selection)
#plotly_plot_list(append(plotly_plot_list(), list(bp3)))
#box_vals$plt <- bp3
return(bp3)
}
})
output$p4 <- renderPlotly({
if (length(input$geneName)<4){
return(NULL)
}else{
selection <- which(input$geneName[4]==colnames(cleaned_data2), arr.ind = TRUE)
bp4 <-  graph_gene(selection)
#plotly_plot_list(append(plotly_plot_list(), list(bp4)))
return(bp4)
}
})
output$p5 <- renderPlotly({
if (length(input$geneName)<5){
return(NULL)
}else{
selection <- which(input$geneName[5]==colnames(cleaned_data2), arr.ind = TRUE)
bp5 <- graph_gene(selection)
#plotly_plot_list(append(plotly_plot_list(), list(bp5)))
return(bp5)
}
})
output$p6 <- renderPlotly({
if (length(input$geneName)<6){
return(NULL)
}else{
selection <- which(input$geneName[6]==colnames(cleaned_data2), arr.ind = TRUE)
bp6 <- graph_gene(selection)
#plotly_plot_list(append(plotly_plot_list(), list(bp6)))
return(bp6)
}
})
######### rendering the table based on dataset choosen #######
## creating reactive table for data set selected
select_dataset_main <- reactive({
debounce(sub_list, 5000)
tbl <- reactive(subset(AE_rna, (gene_names %in% sub_list())))
debounce(tbl, 5000)
tbl()
return(tbl())
})
# Output for the table on the all gene data tab
output$datatable <- renderDataTable(select_dataset_main(),
options = list(scrollX = TRUE, scrollY = TRUE, autoWidth = TRUE)
)
##
select_dataset_npp <- reactive({
debounce(sub_list_NPP, 5000)
tbl1 <- reactive(subset(updated_data_main, (GeneName %in% c("pain state","Age","sex",sub_list_NPP()))))
debounce(tbl1, 5000)
tbl1()
return(tbl1())
})
# Output for the table on the all gene data tab
output$datatable_npp <- renderDataTable(select_dataset_npp(),
options = list(scrollX = TRUE, scrollY = TRUE, autoWidth = TRUE)
)
##############################################################
# This is used to store the plots that will be downloaded
vals <- reactiveValues()
# Create a reactive plot list that will be used to display the plots on the page
plts <- reactive({
if (input$plt == "Dot Plot") {
plt <- DotPlot(drg, features = sub_list())
vals$plt <- plt
return(plt)
} else if (input$plt == "Dim Plot") {
plt <- DimPlot(drg)
vals$plt <- plt
return(plt)
} else if (input$plt == "Violin Plot") {
L = length(sub_list())
plt <- VlnPlot(drg, features = sub_list(), combine = FALSE)
nc = 2
if (L <= 2) {
nc = 1
}
plt1 <- VlnPlot(drg, features = sub_list(), ncol = nc, combine = TRUE)
vals$plt <- plt1
return(plt)
} else if (input$plt == "Ridge Plot") {
L = length(sub_list())
plt <- RidgePlot(drg, features = sub_list(), combine = FALSE)
nc = 2
if (L <= 2) {
nc = 1
}
plt1 <- RidgePlot(drg, features = sub_list(), ncol = nc, combine = TRUE)
vals$plt <- plt1
return(plt)
} else if (input$plt == "Feature Plot") {
L = length(sub_list())
plt <- FeaturePlot(drg, features = sub_list(), combine = FALSE)
nc = 2
if (L <= 2) {
nc = 1
}
plt1 <- FeaturePlot(drg, features = sub_list(), ncol = nc, combine = TRUE)
vals$plt <- plt1
return(plt)
}
})
#trying to make the page length increase with no of selections increase
observe({
shinyjs::toggle("plot2", condition = isTRUE(length(sub_list())>1 && input$plt != "Dot Plot" && input$plt != "Dim Plot"))
shinyjs::toggle("plot3", condition = isTRUE(length(sub_list())>2 && input$plt != "Dot Plot" && input$plt != "Dim Plot"))
shinyjs::toggle("plot4", condition = isTRUE(length(sub_list())>3 && input$plt != "Dot Plot" && input$plt != "Dim Plot"))
shinyjs::toggle("plot5", condition = isTRUE(length(sub_list())>4 && input$plt != "Dot Plot" && input$plt != "Dim Plot"))
shinyjs::toggle("plot6", condition = isTRUE(length(sub_list())>5 && input$plt != "Dot Plot" && input$plt != "Dim Plot"))
})
# Outputs for the first-sixth plot positions
output$plot1 <- renderPlot({
validate(
need(input$geneName,HTML("Please Select the Gene!"))
)
if (input$plt == "Dot Plot" | input$plt == "Dim Plot") {
plts()
} else if (input$plt == "Violin Plot" | input$plt == "Ridge Plot" | input$plt == "Feature Plot") {
plts()[1]
}
})
output$plot2 <- renderPlot({
if (input$plt == "Violin Plot" | input$plt == "Ridge Plot" | input$plt == "Feature Plot") {
plts()[2]
}
})
output$plot3 <- renderPlot({
if (input$plt == "Violin Plot" | input$plt == "Ridge Plot" | input$plt == "Feature Plot") {
plts()[3]
}
})
output$plot4 <- renderPlot({
if (input$plt == "Violin Plot" | input$plt == "Ridge Plot" | input$plt == "Feature Plot") {
plts()[4]
}
})
output$plot5 <- renderPlot({
if (input$plt == "Violin Plot" | input$plt == "Ridge Plot" | input$plt == "Feature Plot") {
plts()[5]
}
})
output$plot6 <- renderPlot({
if (input$plt == "Violin Plot" | input$plt == "Ridge Plot" | input$plt == "Feature Plot") {
plts()[6]
}
})
## creating reactive table for data set selected
select_dataset <- reactive({
if(input$Data_Set == "Spatial Transcriptomics (DRG)"){
#ds_selected <- "DRG_Human_Noiceptor"
return(AE_rna)
}
else if(input$Data_Set=="Neuropathic Pain (DRG)"){
#ds_selected <- "Neuropathic_Pain"
return(updated_data)
}
})
# Output for the table on the all gene data tab
output$dataset_table <- renderDataTable(select_dataset(), options = list(
scrollX = TRUE
)
)
# Output for the download button
output$download_plots <- downloadHandler(
filename = function() {
paste(input$plt,".pdf",sep = "")
},
content = function(file) {
pdf(file, width = 15, height = 15)
print(vals$plt)
dev.off()
}
)
# Output for the download button
output$download_boxplots <- downloadHandler(
filename = function() {
paste("Neuropathic Pain Plots",".pdf",sep = "")
},
content = function(file) {
# Create a PDF file to save all the plots
pdf(NULL)
pdf(file, width = 8, height = 6)
print(box_vals$plt)
dev.off()
}
)
# Creating a reactive expression to generate dynamic text description
dynamic_text <- reactive({
selected_option <- input$Data_Set
# You can use if-else or switch statements to define the dynamic text based on the selection
if (selected_option == "Spatial Transcriptomics (DRG)") {
return(HTML("<p><b>Description:</b> You have selected <i><b><span style='color:#4D0202;'>Spatial Transcriptomics (DRG)</span></b></i> dataset. This data was generated using single-neuron resolution approach, more detais visit- <a href='https://pubmed.ncbi.nlm.nih.gov/35171654/'>Article Link</a>. Here you can find the average gene expression for each of the 12 neuronal subtypes.</p>"))
#return (dataset1_desc)
#return("Description: You selected spatial transcriptomics (DRG) dataset. This data was generated using single-neuron resolution approach (more detais - https://pubmed.ncbi.nlm.nih.gov/35171654/). Here you can find the average gene expression for each of the 12 neuronal subtypes.</p>")
} else if (selected_option == "Neuropathic Pain (DRG)") {
return(HTML("<p><b>Description:</b> You have selected <i><b><span style='color:#4D0202;'>Neuropathic Pain (DRG)</span></b></i> dataset.This data was generated using sequenced human dorsal root ganglia, more details visit- <a href='https://academic.oup.com/brain/article/146/2/749/6648727?login=true'>Journal Link</a>. Here you can find the quantile normalized TPMs of neuron-enriched samples.</p>"))
#return (dataset2_desc)
#return("You have selected Neuropathic Pain (DRG) dataset.")
} else {
return("Selet the Dataset you want to view.")
}
})
# creating a reactive expression to change the dataset for download handler
output$download_dataset <- downloadHandler(
filename = function(){
paste(input$Data_Set,"csv",sep = ".")
},
content = function(file){
write.csv(select_dataset(),file,sep = ",")
}
)
# Display the dynamic text in the textOutput element
output$description_text <- renderUI({
dynamic_text()
})
end <- Sys.time()
print(end - beginning)
})
}
graph_gene <- function(gene){
# turning Pain States in categories
cleaned_data2$painState <- as.factor(cleaned_data2$painState)
# get all the column names and the gene name
col_names <- colnames(cleaned_data2)
gene_name <- col_names[gene] # change this value to change which gene shows up
# load the name of the gene into a variable
gene_data <- as.numeric(cleaned_data2[[gene_name]])
## plotly graph
mrg <- list(l = 55, r = 50,
b = 50, t = 50,
pad = 20)
p <- plot_ly(
data = cleaned_data2,
y = ~gene_data,
x = ~painState,
type = "box",
color = ~sex,
showlegend = TRUE,boxpoints = "all", jitter = 0.3, pointpos = 0, marker = list(color = 'black'), colors = c("#ff5A1D","#366676")
) %>% layout(boxmode = "group",
title = list(text = paste0("<b>",gene_name, "</b>")),
xaxis = list(title = "<b>Pain State</b>",
zeroline = FALSE),
yaxis = list(title = paste0(c(rep("&nbsp;", 20),
"<b>TPM</b>",
rep("&nbsp;", 20),
rep("\n&nbsp;", 1)),
collapse = ""),
zeroline = FALSE,font = list(size = 15), standoff = 50L), margin = mrg,showlegend = TRUE, legend = list(font = list(size = 15)))
config(p, displayModeBar = TRUE, toImageButtonOptions = list(format= 'png', # one of png, svg, jpeg, webp
filename= paste0(gene_name,' - Average TPM Boxplot'),
height= 500,
width= 1000,
scale= 1 ))
}
# Run the application
shinyApp(ui = ui, server = server)
runApp('App1.R')
library(shiny); runApp('App1.R')
runApp()
runApp()
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shiny::runApp()
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# Load required libraries
library(shiny)
library(shinydashboard)
library(shinyWidgets)
library(shinyjs)
library(profvis)
library(shinythemes)
library(DT)
library(Seurat)
library(ggplot2)
library(rpivotTable)
library(tidyverse)
library(gridExtra)
library(cowplot)
library(ggpubr)
library(dplyr)
library(shinyalert)
library(aws.s3)
library(Matrix)
library(readxl)
library(data.table)
library(plotly)
library(shinyvalidate)
library(orca)
library(shinyLP)
library(future)
library(promises)
library(googlesheets4)
options(warn = -1) # help in suppressing the warnings in the console
# Google sheets authentication
gs4_auth(cache=".secrets", email="mark.g.chavez@gmail.com")
ss <- gs4_get("https://docs.google.com/spreadsheets/d/1zQLx_i-Y4sUikD79r6fx8vJFi-TuBWilAlC66HtXXSE/edit?usp=sharing")
# Import the data
#drg <- drg.combined
# read rds
drg <- readRDS("drg.combined.rds") #As already exists in the work space, no need to read a
DefaultAssay(drg) <- "RNA"
AE <- AverageExpression(drg)
AE_rna <- AE$RNA
updated_data <- read_excel("cleaned_data2.xlsx") #As already exists in the work space, no need to read again
#print(class(updated_data))
updated_data_main <- as.data.frame(updated_data)
# clean the data
updated_data <- updated_data[1:20000,]
col_names <- transpose(updated_data[1])
# Load the cleaned data object as cleaned_data2
cleaned_data2 <- readRDS("cleaned_data2.rds")
columnames <- colnames(cleaned_data2[,4:20000])
#Check if there is equal number of the Gene types in the both the data sets or not?
#columnames <- colnames(cleaned_data2[,4:20000])
## All genes addition
gene_check <- read.csv("all_genes.csv")
gene_names1 <- as.character(gene_check$gene_names)
gene_names <- rownames(AE_rna)
dataset_names <- c("Spatial Transcriptomics (DRG)","Neuropathic Pain (DRG)")
#ds_selected <- "DRG_Human_Noiceptor"
plot_list <- c("Dot Plot", "Violin Plot", "Ridge Plot", "Feature Plot", "Dim Plot")
# Adding background color for filter and search in tables
callback <- c(
"$('#DataTables_Table_0_length select').css('background-color', 'white');",
"$('#DataTables_Table_0_filter input').css('background-color', 'white');"
)
callback1 <- c(
"$('#DataTables_Table_1_length select').css('background-color', 'white');",
"$('#DataTables_Table_1_filter input').css('background-color', 'white');"
)
runApp()
runApp()
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