--- title: "analyze_xwas_results" author: "Saideep Gona" date: "2024-06-07" format: html: code-fold: true code-summary: "Show the code" execute: freeze: true warning: false server: shiny description: "After running summary predixcan on different epigeneti marks and disease conditions, we can analyze the results to see if these marks help identify associations with disease." --- ```{r} library (plotly)library (ggplot2)library (shiny)# library(htmlwidgets) = "/beagle3/haky/users/saideep/projects/aracena_modeling/SPrediXcan/sumstats_formatted/asthma_eczema_farreira_susie/enformer_preds/spredixcan/spredixcan_results_full.txt" = read.table (path_to_full_spredixcan_results, header = T, sep = " \t " )print (head (full_spredixcan_results))$ neglog10p = - log10 (full_spredixcan_results$ pvalue)``` ```{r} library (ggplot2)# ggplot(full_spredixcan_results, aes(x=modality, y=gene, fill=pvalue)) + # geom_raster() + # theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), # lables vertical # strip.text.y = element_blank()) + #remove facet bar on y # scale_fill_gradient(low = "darkblue", high = "lightblue") + # ggtitle("XWAS of fine-mapped Allergy Eczema Loci") + # facet_grid(cols = vars(full_spredixcan_results$condition), scales = "free", space="free_y") #facets to add gaps ``` ```{r} # library(ggplot2) # ggplot(full_spredixcan_results, aes(x=condition, y=gene, fill=pvalue)) + # geom_raster() + # theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), # lables vertical # strip.text.y = element_blank()) + #remove facet bar on y # scale_fill_gradient(low = "darkblue", high = "lightblue") + # ggtitle("XWAS of fine-mapped Allergy Eczema Loci") + # facet_grid(cols = vars(full_spredixcan_results$modality), scales = "free", space="free_y") #facets to add gaps ``` ```{r} # ggplot(full_spredixcan_results, aes(x=modality, y=gene, fill=neglog10p)) + # geom_raster() + # theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), # lables vertical # strip.text.y = element_blank()) + #remove facet bar on y # scale_fill_gradient(low = "darkblue", high = "lightblue") + # ggtitle("XWAS of fine-mapped Allergy Eczema Loci") + # facet_grid(cols = vars(full_spredixcan_results$condition), scales = "free", space="free_y") #facets to add gaps ``` ```{r} # ggplot(full_spredixcan_results, aes(x=condition, y=gene, fill=neglog10p)) + # geom_raster() + # theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), # lables vertical # strip.text.y = element_blank()) + #remove facet bar on y # scale_fill_gradient(low = "darkblue", high = "lightblue") + # ggtitle("XWAS of fine-mapped Allergy Eczema Loci") + # facet_grid(cols = vars(full_spredixcan_results$modality), scales = "free", space="free_y") #facets to add gaps ``` ```{r} # #| context: server # library(plotly) # library(ggplot2) # library(htmlwidgets) # # start of copy # inter_plot1 <- ggplot(full_spredixcan_results, aes(x=condition, y=gene, fill=neglog10p)) + # geom_raster() + # # theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), # lables vertical # # strip.text.y = element_blank()) + #remove facet bar on y # scale_fill_gradient(low = "darkblue", # high = "lightblue") + # ggtitle("XWAS of fine-mapped Allergy Eczema Loci") + # facet_grid(cols = vars(full_spredixcan_results$modality), scales = "free", space="free_y") #facets to add gaps # fig_plotly <- ggplotly(inter_plot1) # fig_plotly <- fig_plotly %>% # layout( # hovermode = "closest", # hoverlabel = list(bgcolor = "white", # font = list(family = "Arial", size = 16, color = "black") # ) # ) # for(i in seq_along(fig_plotly$x$data)){ # fig_plotly <- fig_plotly %>% # add_trace( # data = fig_plotly$x$data[[i]], # x - fig_plotly$x$data[[i]]$x, # y = fig_plotly$x$data[[i]]$y, # type = "scatter", # mode = "markers", # marker = list(color = fig_plotly$x$data[[i]]$fill), # hoverinfo = "text", # text = ~paste("Gene: ", gene, "<br>Condition: ", condition, "<br>Modality: ", modality, "<br>neglog10p: ", neglog10p) # ) # } # # end of copy # # trace delete # # fig_plotly$x$data <- fig_plotly$x$data[seq_len(length(fig_plotly$x$data) - 1)] # # fig_plotly$x$data <- fig_plotly$x$data[seq_len(length(fig_plotly$x$data)/2)] # fig_plotly ``` ```{r} # UI for Shiny app # ui <- fluidPage( # titlePanel(“Population by Continent Over Time”), # sidebarLayout( # mainPanel( # plotlyOutput(“plot”) # ) # ) # ) # ui <- navbarPage("test", # tabPanel("test_tab"), # mainPanel( # plotlyOutput("plot") # ) # ) # print(ui) ``` ```{r, xwas_app_v2_working_version} # library(shiny) # library(plotly) # library(ggplot2) # library(htmlwidgets) # ui <- navbarPage("XWAS App", # tabPanel("XWAS Tab Name"), # mainPanel( # plotlyOutput("plot") # ) # ) # # Server logic for Shiny app # server <- function(input, output) { # # Reactive expression for filtered data # # filtered_data <- reactive({ # # df_cnt %>% filter(year == input$year) # # }) # #plot1 # output$plot <- renderPlotly({ # inter_plot1 <- ggplot(full_spredixcan_results, aes(x=condition, y=gene, # text = paste("Gene: ", gene, "<br>Condition: ", condition, "<br>Modality: ", modality, "<br>neglog10p: ", neglog10p), # # text = paste("Modality: ", modality), # fill=neglog10p)) + # geom_raster() + # # theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), # lables vertical # # strip.text.y = element_blank()) + #remove facet bar on y # scale_fill_gradient(low = "darkblue", # high = "lightblue") + # ggtitle("XWAS of fine-mapped Allergy Eczema Loci") + # facet_grid(cols = vars(full_spredixcan_results$modality), scales = "free", space="free_y") #facets to add gaps # fig_plotly <- ggplotly(inter_plot1, tooltip="text") # }) # } # # Run the Shiny app # shinyApp(ui = ui, server = server) ``` ```{r, xwas_app_v3} # # library(shiny) # # library(plotly) # # library(ggplot2) # # library(htmlwidgets) # library(plotly) # library(ggplot2) # library(shiny) # # library(htmlwidgets) # path_to_full_spredixcan_results = "/beagle3/haky/users/saideep/projects/aracena_modeling/SPrediXcan/sumstats_formatted/asthma_eczema_farreira_susie/enformer_preds/spredixcan/spredixcan_results_full.txt" # full_spredixcan_results = read.table(path_to_full_spredixcan_results, header = T, sep = "\t") # print(head(full_spredixcan_results)) # full_spredixcan_results$neglog10p = -log10(full_spredixcan_results$pvalue) # # define global variables # facet_variables <- c("condition", "modality") # x_variables <- c("condition", "modality") # ui <- navbarPage("XWAS App", # tabPanel("XWAS Tab Name", # sidebarPanel(t # h4("Change Plot Settings"), # selectInput("select_facet_variable", # "Select facet variable", # choices = facet_variables, # selected = choices[1] # ), # selectInput("select_x_variable", # "Select x variable", # choices = x_variables, # selected = choices[1] # ) # ), # mainPanel( # h3("XWAS of fine-mapped Allergy Eczema Loci"), # plotlyOutput("plot1") # ) # ) # ) # # ui <- navbarPage("XWAS App", # # tabPanel("XWAS Tab Name", # # sidebarPanel( # # h4("Change Plot Settings"), # # selectInput("select_facet_variable", # # "Select facet variable", # # choices = facet_variables, # # selected = choices[1] # # ), # # selectInput("select_x_variable", # # "Select x variable", # # choices = x_variables, # # selected = choices[1] # # ) # # ) # # ), # # mainPanel( # # plotlyOutput("plot1") # # ) # # ) # # Server logic for Shiny app # server <- function(input, output, session) { # # Reactive expression for filtered data # # filtered_data <- reactive({ # # df_cnt %>% filter(year == input$year) # # }) # #Used to observe the facet and x selection # observe({ # facet_variable <- input$select_facet_variable # second_x_variable <- facet_variables[facet_variables != facet_variable] # updateSelectInput(session, # 'select_x_variable', # choices = second_x_variable, # selected = second_x_variable[1]) # }) # #Plot1 - Facet = Modality, x = condition # output$plot1 <- renderPlotly({ # inter_plot1 <- ggplot(full_spredixcan_results, aes(x=input$select_x_variable, y=gene, # text = paste("Gene: ", gene, "<br>Condition: ", condition, "<br>Modality: ", modality, "<br>neglog10p: ", neglog10p), # # text = paste("Modality: ", modality), # fill=neglog10p)) + # geom_raster() + # # theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), # lables vertical # # strip.text.y = element_blank()) + #remove facet bar on y # scale_fill_gradient(low = "darkblue", # high = "lightblue") + # ggtitle("XWAS of fine-mapped Allergy Eczema Loci") + # facet_grid(cols = vars(full_spredixcan_results$input$select_facet_variable), scales = "free", space="free_y") #facets to add gaps # fig_plotly <- ggplotly(inter_plot1, tooltip="text") # }) # } # # Run the Shiny app # shinyApp(ui = ui, server = server) ``` ```{r, hover_text_with_R_and_plotly} # inter_plot1 <- ggplot(full_spredixcan_results, aes(x=condition, y=gene, # # text = paste("Gene: ", gene, "<br>Condition: ", condition, "<br>Modality: ", modality, "<br>neglog10p: ", neglog10p), # text = paste("Modality: ", modality), # fill=neglog10p)) + # geom_raster() + # # theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), # lables vertical # # strip.text.y = element_blank()) + #remove facet bar on y # scale_fill_gradient(low = "darkblue", # high = "lightblue") + # ggtitle("XWAS of fine-mapped Allergy Eczema Loci") + # facet_grid(cols = vars(full_spredixcan_results$modality), scales = "free", space="free_y") #facets to add gaps # fig_plotly <- ggplotly(inter_plot1) # fig_plotly ``` ```{r, xwas_app_v4_latest_working_app} #| context: server library (shiny)library (plotly)# Sample data for select inputs <- c ("condition" , "modality" )<- c ("condition" , "modality" )<- c ("neglog10p" , "var_g" )<- navbarPage ("XWAS App" ,tabPanel ("XWAS Tab Name" ,sidebarPanel (h4 ("Change Plot Settings" ),selectInput ("select_facet_variable" ,"Select facet variable" ,choices = facet_variables,selected = facet_variables[1 ]selectInput ("select_x_variable" ,"Select x variable" ,choices = x_variables,selected = x_variables[1 ]selectInput ("select_color_variable" ,"Select color variable" ,choices = color_variables,selected = color_variables[1 ]mainPanel (h3 ("XWAS of fine-mapped Allergy Eczema Loci" ),plotlyOutput ("plot1" )<- function (input, output, session) {observe ({<- input$ select_facet_variable<- facet_variables[facet_variables != facet_variable]updateSelectInput (session,'select_x_variable' ,choices = second_x_variable,selected = second_x_variable[1 ])# Plot1 - Facet = Modality, x = condition $ plot1 <- renderPlotly ({<- ggplot (full_spredixcan_results, aes_string (x = input$ select_x_variable, y = "gene" , # text = paste( # "Gene: ", full_spredixcan_results$gene, # "<br>Condition: ", full_spredixcan_results$condition, # "<br>Modality: ", full_spredixcan_results$modality, # "<br>neglog10p: ", full_spredixcan_results$neglog10p), fill = input$ select_color_variable+ geom_raster (aes (text = paste ("Gene: " , gene, "<br>Condition: " , condition, "<br>Modality: " , modality,"<br>neglog10p: " , neglog10p)+ scale_fill_gradient (low = "darkblue" , high = "lightblue" ) + ggtitle ("XWAS of fine-mapped Allergy Eczema Loci" ) + facet_grid (cols = vars (full_spredixcan_results[[input$ select_facet_variable]]), scales = "free" , space = "free_y" )<- ggplotly (inter_plot1, tooltip = "text" )shinyApp (ui = ui, server = server)``` 
