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677 lines
30 KiB
677 lines
30 KiB
library(shiny)
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# library(ggplot2)
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library(reshape2)
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library(openxlsx)
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# library(dplyr)
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library(car)
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library(ggbeeswarm)
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library(gtools)
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library(gridExtra)
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source("../../funcions.R")
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library(survminer)
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library(survival)
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library(plotrix)
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library(tidyverse)
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library(ggpubr)
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library(rstatix)
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# Define UI for application
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ui <- fluidPage(
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#Navbar
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navbarPage("Seguimiento in vivos",
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tabPanel("Diseño",
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sidebarPanel(
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fileInput(inputId = "file_sizes", label = "Hoja de tamaños", multiple = F),
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selectInput(inputId = "measure_sys", "Sistema de medida", selected = "L-W-D", choices = c("L-W-D","Min-Max","Absorbance")),
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uiOutput('ncages'),
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uiOutput('lowcut'),
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uiOutput('upcut'),
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uiOutput('goButton'),
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uiOutput('iterations'),
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downloadButton("downloadData", "Descargar Excel")
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),
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mainPanel(
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plotOutput("firstPlot"),
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plotOutput("distPlot"),
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tableOutput("distTable")
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)
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),
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tabPanel("Análisis",
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sidebarPanel(
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fileInput(inputId = "file_analy", label = "Hoja de análisis", multiple = F),
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selectInput(inputId = "vacc", "Experimento de Vacunación", selected = "No", choices = c("Sí","No")),
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uiOutput('cutoffUI'),
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checkboxInput("filter_stats","Filtrar Estadística"),
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downloadButton("downloadVolume", "Descargar Volúmenes")
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),
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mainPanel(
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h3('Ratones'),
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tableOutput('ntable'),
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h3('Cinéticas'),
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plotOutput('cin_group'),
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plotOutput('cin_indiv'),
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plotOutput('survival', height="800px"),
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h3('Estadística'),
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verbatimTextOutput('stats'),
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tableOutput('tab_stats')
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)),
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tabPanel("Exportar",
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sidebarPanel(width=2,
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h3('Seleccionar figura'),
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selectInput("fig_id", "", selected="", choices=c("Cinética Grupo", "Cinética Individual", "Survival")),
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h3('Formato'),
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sliderInput("width", "Ancho", min=1000, max=20000, step=1000, value=10000),
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sliderInput("height", "Altura", min=1000, max=20000, step=1000, value=6000),
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textInput("colors", label="Colors", value=""),
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sliderInput("errorbar-width", "% Ancho errorbars", min=0.05, max=1, step=0.05, value=0.05),
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sliderInput("point-size", "Tamaño puntos", min=1, max=10, step=1, value=3),
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sliderInput("font-size", "Tamaño textos", min=5, max=30, step=1, value=11),
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checkboxInput(inputId = "logscale", label="Escala logarítmica eje Y", value=F),
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checkboxInput(inputId = "legend", label = "Mostrar llegenda", value = T),
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selectInput("theme", "Seleccionar Tema", selected="BW", choices=c("BW", "Default", "Classic")),
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downloadButton("downloadPicture", "Exportar")
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),
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mainPanel(
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uiOutput("expPlotUI")
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)
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)
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)
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)
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# Define server logic required to draw a histogram
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server <- function(input, output) {
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# Diseño
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dades<-reactiveValues()
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dades$taula<-NULL
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dades$groups<-NULL
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dades$db<-NULL
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observe({
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if (!is.null(input$file_sizes)){
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taula<-read.xlsx(input$file_sizes$datapath, sheet = 1, sep.names = " ")
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if ("DPV" %in% colnames(taula)){
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taula<-dcast(taula, Cage+`ID animal`+`ID tumor`+Group~DPV, value.var = "0")
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# taula$Major<-taula$Major/1000
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# taula$Minor<-taula$Minor/1000
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# taula["Volume"]<-((taula$Major*taula$Minor*taula$Minor)*(pi/6))*1000
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taula$Major<-taula$Major
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taula$Minor<-taula$Minor
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taula["Volume"]<-((taula$Major*taula$Minor*taula$Minor)*(pi/6))
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}
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dades$taula<-taula
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dades$groups<-read.xlsx(input$file_sizes$datapath, sheet = 2)[,1]
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}
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})
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output$firstPlot <- renderPlot({
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observeEvent(dades$taula, {})
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if (!is.null(dades$taula)){
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ggplot(dades$taula, aes(x="1", y=Volume))+geom_hline(yintercept = c(input$lowcut, input$upcut), color="red")+geom_quasirandom(width=0.2)
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}
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})
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output$iterations<-renderUI({
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if (!is.null(dades$taula)){
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sliderInput("iterations", "Iteraciones", min=100, max=2000, step=100, value=100)
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}
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})
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output$lowcut<-renderUI({
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if (!is.null(dades$taula)){
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cut.max<-round(max(dades$taula$Volume, na.rm = T), 2)
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step<-round(max(dades$taula$Volume, na.rm = T)/20, 2)
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sliderInput("lowcut", "Corte inferior", min=0, max=cut.max, step=step, value=0)
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}
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})
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output$upcut<-renderUI({
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if (!is.null(dades$taula)){
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cut.max<-round(max(dades$taula$Volume, na.rm = T), 2)+0.01
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step<-round(max(dades$taula$Volume, na.rm = T)/20, 2)
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sliderInput("upcut", "Corte superior", min=0, max=cut.max, step=step, value=cut.max)
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}
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})
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output$goButton<-renderUI({
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if (!is.null(dades$taula)){
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actionButton("goButton", "Selecciona")
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}
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})
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output$ncages<-renderUI({
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if (is.null(dades$taula)){
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sliderInput("ncages", "Cajas", min=1, max=10, value=1)
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}
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})
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grafic<-eventReactive(input$goButton,{
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df<-dades$taula
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df<-df[!is.na(df$Volume),]
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up_cuttof<-input$upcut
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low_cuttof<-input$lowcut
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print(up_cuttof)
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df<-df[df$Volume < up_cuttof & df$Volume >= low_cuttof,]
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# df["Mouse"]<-gsub("[a-zA-Z]", "", df$MouseID)
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print(df$Volume)
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s<-shapiro.test(df$Volume)[[2]]
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ngroup<-length(dades$groups)
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ind.list<-list()
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pval.list<-list()
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lvn.list<-list()
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test.list<-list()
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for (data in 1:input$iterations){
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interr=T
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while(interr == T){
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ind<-sample(rep(dades$groups, each=ceiling(length(unique(df$`ID animal`))/ngroup)), length(unique(df$`ID animal`)))
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df_temp<-merge(df[,c("ID animal", "ID tumor","Volume")], data.frame("ID animal"=unique(df$`ID animal`), "group"=as.factor(ind),check.names=F))
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if ((nrow(df_temp)/ngroup) %% 2 == 0){
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interr<-any(table(df_temp$group) < floor(nrow(df_temp)/ngroup) | table(df_temp$group) > ceiling(nrow(df_temp)/ngroup))
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}else{
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# interr<-any(table(df_temp$group) < (floor(nrow(df_temp)/ngroup)-1) | table(df_temp$group) > (ceiling(nrow(df_temp)/ngroup)+1))
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interr<-diff(range(table(ind))) > 1
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}
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}
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ind.list[[data]]<-df_temp[,c("ID animal", "ID tumor","group","Volume")]
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lvn.list[data]<-leveneTest(Volume ~ group, data = df_temp[,3:4])[[2]][1]
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if (s < 0.05){
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k<-kruskal.test(df_temp$Volume,df_temp$group)
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test.list[data]<-k[[1]][1]
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pval.list[data]<-k[[3]][1]
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}else{
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res.aov<-aov(Volume~group, data=df_temp)
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pval.list[data]<-summary(res.aov)[[1]][[5]][1]
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test.list[data]<-summary(res.aov)[[1]][[4]][1]
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}
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}
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index<-which(unlist(lvn.list) == min(unlist(lvn.list)[which(unlist(pval.list) %in% sort(unlist(pval.list), decreasing = T)[1:20])]))
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df_def<-merge(df, ind.list[[index]])
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if ("Group" %in% colnames(df_def)){
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df_def<-df_def %>% select(-"Group")
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}
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df_def<-merge(dades$taula %>% select(-Group), df_def[,c("ID animal", "group")] %>% unique, all=T, by="ID animal") %>% select(c(`ID animal`, `ID tumor`, Volume, Cage, Major, Minor, group))
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df_def[!paste0(df_def$`ID animal`, df_def$`ID tumor`) %in% paste0(df$`ID animal`, df$`ID tumor`),"group"]<-NA
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dades$db<-df_def
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ggplot(df_def, aes(group, Volume))+
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geom_boxplot(outlier.alpha = F)+
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geom_jitter(width=0.25)+
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geom_point(stat="summary", color="blue", size=3)
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# lims(y=c(0,max(df_def$Volume)+10))
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})
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output$distPlot <- renderPlot({
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observeEvent(dades$taula, {})
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if (!is.null(dades$taula)){
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grafic()
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}
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})
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output$distTable <- renderTable({
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observeEvent(dades$db, {})
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if (!is.null(dades$db)){
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df<-dades$db
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df_sum<-dcast(df, group~., value.var = "Volume", fun.aggregate = mean, na.rm=T) %>% rename("Mean"=".")
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df_sum["SEM"]<-dcast(df, group~., value.var = "Volume", fun.aggregate = std.error, na.rm=T) %>% pull(`.`)
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df_sum
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}
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})
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output$downloadData <- downloadHandler(
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filename = function() {
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paste("invivo", ".xlsx", sep="")
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},
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content = function(file){
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ncages<-input$ncages
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nrat_cage<-5
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id_tumors<-c("L","R")
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timepoint<-c(7,10,13,16,19,22,25)
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if (!is.null(input$file_sizes)){
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dtemplate<-dades$db %>% select(-Volume)
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dtemplate<-melt(dtemplate, id=c("Cage", "ID animal", "ID tumor", "group"), variable.name = "DPV", value.name = "0") %>% rename("Group"="group")
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dtemplate<-dtemplate[,c("Cage", "ID animal", "ID tumor", "Group", "DPV", "0")] %>% arrange(`ID animal`, `ID tumor`)
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# template<-expand.grid(dades$db$MouseID, timepoint)
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# colnames(template)<-c("MouseID", "Timepoint")
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# template<-template[order(template$Timepoint, template$MouseID),]
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# template<-merge(template, dades$db[c("MouseID", "group")])
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# if (input$measure_sys == "L-W-D"){
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# template<-rbind(template, template, template)
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# template<-template[order(template$Timepoint, template$MouseID),]
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# template["TS"]<-rep(c("TS-Length", "TS-Width", "TS-Deep"), nrow(template)/3)
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# dtemplate<-dcast(template, MouseID+group+TS~Timepoint)
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# dtemplate<-dtemplate[mixedorder(as.character(dtemplate$MouseID)),]
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# }
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# if (input$measure_sys == "Min-Max"){
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# template<-rbind(template, template)
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# template<-template[order(template$Timepoint, template$MouseID),]
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# template["DPV"]<-rep(c("Major", "Minor"), nrow(template)/2)
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# dtemplate<-dcast(template, MouseID+group+DPV~Timepoint)
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# dtemplate<-dtemplate[mixedorder(as.character(dtemplate$MouseID)),]
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# }
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# dtemplate<-dtemplate %>% add_column(.after="MouseID", "ID tumor"=dtemplate$MouseID)%>% rename(`ID animal`=MouseID)
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# dtemplate["ID tumor"]<-gsub("[[:digit:]]","",dtemplate$`ID tumor`)
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# dtemplate["ID animal"]<-gsub("[LR]","",dtemplate$`ID animal`)
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# dtemplate[,5:ncol(dtemplate)]<-""
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}else{
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template<-expand.grid(LETTERS[1:ncages], 1:5, id_tumors, timepoint)[,-2]
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colnames(template)<-c("Cage", "ID tumor", "Timepoint")
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nids<-length(id_tumors)*length(timepoint)
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template[order(template$Cage),"ID animal"]<-rep(1:(nrow(template)/(nids)), each=nids)
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template<-template[order(template$Timepoint, template$Cage, template$`ID animal`),]
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template["Group"]<-""
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if (input$measure_sys == "L-W-D"){
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template<-rbind(template, template, template)
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template<-template[order(template$Timepoint, template$Cage, template$`ID animal`, template$`ID tumor`),]
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template["TS"]<-rep(c("TS-Length", "TS-Width", "TS-Deep"), nrow(template)/3)
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dtemplate<-dcast(template, Cage+`ID animal`+`ID tumor`+Group+TS~Timepoint)
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dtemplate<-dtemplate[order(match(dtemplate$TS, c("TS-Length", "TS-Width", "TS-Deep"))),]
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dtemplate<-dtemplate[mixedorder(dtemplate$`ID tumor`),]
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dtemplate<-dtemplate[mixedorder(dtemplate$`ID animal`),]
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}
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if (input$measure_sys == "Min-Max"){
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template<-rbind(template, template)
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template<-template[order(template$Timepoint, template$Cage, template$`ID animal`, template$`ID tumor`),]
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template["DPV"]<-rep(c("Major", "Minor"), nrow(template)/2)
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dtemplate<-dcast(template, Cage+`ID animal`+`ID tumor`+Group+DPV~Timepoint)
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}
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dtemplate[,6:ncol(dtemplate)]<-""
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}
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write.xlsx(dtemplate,file)
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}
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)
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# Análisis
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analysis<-reactiveValues()
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analysis$taula<-NULL
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analysis$taula_def<-NULL
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analysis$taula_vol<-NULL
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observe({
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if (!is.null(input$file_analy)){
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analysis$taula<-read.xlsx(input$file_analy$datapath, sheet = 1, check.names = F, sep.names = " ")
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}
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})
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output$cutoffUI<-renderUI({
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if (!is.null(analysis$taula_def)){
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observeEvent(analysis$taula_def, {})
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max_val<-max(analysis$taula_def$Volume, na.rm = T)
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print(max_val)
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sliderInput("cutoff", "Cutoff para Survival", min=0, max=round(max_val), step=round(max_val)/200, value=max_val)
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}
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})
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output$ntable<-renderTable({
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if (!is.null(input$file_analy)){
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observeEvent(analysis$taula, {})
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stattest<-"dunn"
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oneside<-""
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table<-analysis$taula
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if ("ID.animal" %in% colnames(table)){table<-rename(table, "ID animal"=`ID.animal`)}
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if ("ID" %in% colnames(table)){table<-rename(table, "ID animal"=ID)}
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if ("ID.tumor" %in% colnames(table)){table<-rename(table, "ID tumor"=`ID.tumor`)}
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# table[table$ID.tumor == "R","0"]<-NA
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col_nodays<-c("ID","Code", "Cage","Group", "ID.animal","ID animal", "ID.tumor", "ID tumor", "TS","DPV", "Absorbance")
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if (length(grep(0, colnames(table)[!colnames(table) %in% col_nodays])) == 0){
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table["0"]<-0
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}
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table<-melt(table, id=colnames(table)[colnames(table) %in% col_nodays], variable.name = "Timepoint")
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table$Timepoint<-gsub("[A-Za-z ]","",table$Timepoint)
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print(table)
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if ("DPV" %in% colnames(table)){
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table<-dcast(table, Cage+`ID animal`+`ID tumor`+Group+Timepoint~DPV, value.var = "value")
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table$Major<-table$Major
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table$Minor<-table$Minor
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table["Volume"]<-((table$Major*table$Minor*table$Minor)*(pi/6))
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}
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if ("TS" %in% colnames(table)){
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table<-dcast(table, Cage+`ID animal`+`ID tumor`+Group+Timepoint~TS, value.var = "value")
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table["Volume"]<-table$`TS-Deep`*table$`TS-Length`*table$`TS-Width`*pi/6
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}
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if (!"Volume" %in% colnames(table)){table<-rename(table, "Volume"=value)}
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table<-table %>% filter(!is.na(Group))
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table$Timepoint<-factor(table$Timepoint, levels=mixedsort(as.numeric(as.character(unique(table$Timepoint)))))
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analysis$taula_def<-table
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analysis$taula_vol<-dcast(table, Cage+`ID animal`+`ID tumor`~Timepoint,value.var = "Volume")
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table_plot<-dcast(dcast(table %>% filter(!is.na(Volume)), `ID animal`+Group+Timepoint~., value.var = "Volume", fun.aggregate = mean), Group~Timepoint)
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table_plot
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}
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})
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output$cin_group<-renderPlot({
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if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
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observeEvent(analysis$taula_def, {})
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table<-analysis$taula_def
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if (input$vacc == "Sí"){
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ggplot(table, aes(as.numeric(as.character(Timepoint)), Volume, color=Group, group=Group))+
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geom_errorbar(stat="summary", width=0.05)+
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geom_line(stat="summary")+
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geom_point(stat="summary")+
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facet_grid(factor(`ID tumor`, labels = c("Vaccination", "Rechallenge"))~., scale="free_y")+
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labs(x="Days after tumor challenge")+
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scale_y_continuous(expand = expansion(mult = c(0,0.05)))+
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scale_x_continuous(expand = expansion(mult = c(0,0.05)), limits = c(0, (round(max(as.numeric(as.character(table$Timepoint))) / 5)+1)*5))+
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theme_bw()
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}else{
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ggplot(table, aes(Timepoint, Volume, color=Group, group=Group))+
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geom_errorbar(stat="summary",width=0.05)+
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geom_line(stat="summary")+
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geom_point(stat="summary")+
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labs(x="Days after tumor challenge")+
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scale_y_continuous(expand = expansion(mult = c(0,0.05)))+
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theme_bw()+
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theme(axis.text.x=element_text(angle=45, hjust=1))
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}
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}
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})
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output$cin_indiv<-renderPlot({
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if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
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observeEvent(analysis$taula_def, {})
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table<-analysis$taula_def
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print(table , )
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if (input$vacc == "Sí"){
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ggplot(table, aes(as.numeric(as.character(Timepoint)), Volume, color=Group, group=`ID animal`))+
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# geom_errorbar(stat="summary", width=0.05)+
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geom_line()+
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geom_point()+
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scale_y_continuous(expand = expansion(mult = c(0,0.05)))+
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scale_x_continuous(expand = expansion(mult = c(0,0.05)), limits = c(0, (round(max(as.numeric(as.character(table$Timepoint))) / 5)+1)*5))+
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facet_grid(factor(`ID tumor`, labels = c("Vaccination", "Rechallenge"))~Group, scale="free_y")+
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labs(x="Days after tumor challenge")+
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theme_bw()
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}else{
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ggplot(table, aes(Timepoint, Volume, color=Group, group=`ID animal`))+
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# geom_errorbar(stat="summary", width=0.05)+
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geom_line()+
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geom_point()+
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scale_y_continuous(expand = expansion(mult = c(0,0.05)))+
|
|
# scale_x_continuous(expand = expansion(mult = c(0,0.05)), limits = c(0, (round(max(as.numeric(as.character(table$Timepoint))) / 5)+1)*5))+
|
|
facet_wrap(.~Group)+
|
|
labs(x="Days after tumor challenge")+
|
|
theme_bw()+
|
|
theme(axis.text.x=element_text(angle=45, hjust=1))
|
|
}
|
|
}
|
|
})
|
|
|
|
output$survival<-renderPlot({
|
|
if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
|
|
observeEvent(analysis$taula_def, {})
|
|
table<-analysis$taula_def
|
|
if (input$vacc == "Sí"){
|
|
g<-list()
|
|
for (side in c("L","R")){
|
|
tableR<-filter(table, `ID tumor` == side) %>% filter(!is.na(Volume))
|
|
endtime<-dcast(tableR %>% filter(Volume < input$cutoff), Cage+`ID animal`+`ID tumor`+Group~., value.var = "Timepoint", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
|
|
endtime["Dead"]<-dcast(tableR, Cage+`ID animal`+`ID tumor`+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") > input$cutoff
|
|
table_tumor<<-endtime
|
|
|
|
g[side]<-ggsurvplot(survfit(Surv(table_tumor$end, table_tumor$Dead) ~ table_tumor$Group, data=table_tumor),
|
|
pval = T, pval.method = T,
|
|
title = side,
|
|
# legend.labs = paste(c("< median", ">= median"), "MICA"),
|
|
ggtheme=theme_classic(base_size=15)
|
|
# conf.int = TRUE,
|
|
# Add risk table
|
|
# risk.table = TRUE,
|
|
# tables.height = 0.2,
|
|
# tables.theme = theme_cleantable(),
|
|
|
|
# Color palettes. Use custom color: c("#E7B800", "#2E9FDF"),
|
|
# or brewer color (e.g.: "Dark2"), or ggsci color (e.g.: "jco")
|
|
# palette = c("#E7B800", "#2E9FDF")
|
|
)
|
|
}
|
|
do.call(grid.arrange, g)
|
|
|
|
}else{
|
|
tableR<-table %>% filter(!is.na(Volume))
|
|
endtime<-dcast(tableR %>% filter(Volume < input$cutoff), `ID animal`+Group~., value.var = "Timepoint", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
|
|
endtime["Dead"]<-dcast(tableR, `ID animal`+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") > input$cutoff
|
|
table_tumor<<-endtime
|
|
|
|
g<-ggsurvplot(survfit(Surv(table_tumor$end, table_tumor$Dead) ~ table_tumor$Group, data=table_tumor),
|
|
pval = T, pval.method = T,
|
|
# legend.labs = paste(c("< median", ">= median"), "MICA"),
|
|
ggtheme=theme_classic(base_size=15)
|
|
# palette = c("#E7B800", "#2E9FDF")
|
|
)
|
|
g
|
|
}
|
|
}
|
|
})
|
|
|
|
output$stats<-renderPrint({
|
|
stattest<-"dunn"
|
|
oneside<-""
|
|
if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
|
|
observeEvent(analysis$taula_def, {})
|
|
table<-analysis$taula_def
|
|
if (input$vacc == "No"){
|
|
table<-filter(table, !is.na(Volume))
|
|
summary(aov(Volume~Group+Timepoint+Error(`ID animal`), data=table))
|
|
}else{
|
|
for (side in c("L","R")){
|
|
tableR<-filter(table, `ID tumor` == side) %>% filter(!is.na(Volume))
|
|
if (length(unique(tableR$Volume)) > 1 & length(unique(tableR$Timepoint)) > 1){
|
|
print(paste0("Side: ",side))
|
|
# print(summary(aov(Volume~Group+Timepoint+Error(paste0(ID animal,Cage)), data=tableR)))
|
|
print(summary(aov(Volume~Group+Timepoint+Error(`ID animal`), data=tableR)))
|
|
}
|
|
}
|
|
}
|
|
}
|
|
})
|
|
output$tab_stats<-renderTable({
|
|
stattest<-"dunn"
|
|
oneside<-""
|
|
if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
|
|
table<-analysis$taula_def
|
|
table_stats<-list()
|
|
if (input$vacc == "No"){
|
|
table<-table%>%filter(!is.na(Volume))
|
|
if (length(unique(table$Volume)) > 1){
|
|
table_stats<-multi_stats(table, "Volume", "Timepoint", "Group", stat.test=stattest)
|
|
}
|
|
table_kw<-as.data.frame(matrix(nrow=0, ncol=2))
|
|
for (point in unique(table$Timepoint)){
|
|
len_group<-length(unique(table %>% filter(Timepoint == point) %>% pull(Group)))
|
|
if (len_group > 1){
|
|
table_kw<-rbind(table_kw, data.frame(point,kruskal.test(table %>% filter(Timepoint == point) %>% pull(Volume), table %>% filter(Timepoint == point) %>% pull(Group))[3][[1]]))
|
|
}
|
|
}
|
|
colnames(table_kw)<-c("Timepoint", "KW-p.val")
|
|
table_stats<-merge(table_stats, table_kw)
|
|
|
|
}else{
|
|
for (side in c("L","R")){
|
|
tableR<-filter(table, `ID tumor` == side) %>% filter(!is.na(Volume))
|
|
if (length(unique(tableR$Volume)) > 1){
|
|
table_stats[[side]]<-multi_stats(tableR, "Volume", "Timepoint", "Group", stat.test=stattest)
|
|
}
|
|
table_kw<-as.data.frame(matrix(nrow=0, ncol=2))
|
|
for (point in unique(tableR$Timepoint)){
|
|
len_group<-length(unique(tableR %>% filter(Timepoint == point) %>% pull(Group)))
|
|
if (len_group > 1){
|
|
table_kw<-rbind(table_kw, data.frame(point,kruskal.test(tableR %>% filter(Timepoint == point) %>% pull(Volume), tableR %>% filter(Timepoint == point) %>% pull(Group))[3][[1]]))
|
|
}
|
|
}
|
|
colnames(table_kw)<-c("Timepoint", "KW-p.val")
|
|
table_stats[[side]]<-merge(table_stats[[side]], table_kw)
|
|
}
|
|
}
|
|
table_stats_def<-bind_rows(table_stats, .id = "ID tumor")
|
|
if (input$filter_stats == T){
|
|
table_stats_def %>% filter(p.adj < 0.05)
|
|
}else{
|
|
table_stats_def
|
|
}
|
|
}
|
|
})
|
|
|
|
output$downloadVolume <- downloadHandler(
|
|
|
|
filename = function() {
|
|
paste("invivo-vols", ".xlsx", sep="")
|
|
},
|
|
content = function(file){
|
|
dtemplate<-analysis$taula_vol
|
|
write.xlsx(dtemplate,file)
|
|
}
|
|
)
|
|
|
|
##Exportar
|
|
output$expPlotUI<- renderUI({
|
|
observeEvent(analysis$taula_def, {})
|
|
if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
|
|
plotOutput("expPlot", width=paste0(input$width/10,"px"), height = paste0(input$height/10, "px"))
|
|
}
|
|
})
|
|
|
|
output$expPlot <- renderPlot({
|
|
observeEvent(analysis$taula_def, {})
|
|
if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
|
|
table<-analysis$taula_def
|
|
|
|
if (input$fig_id %in% c("Cinética Grupo", "Cinética Individual")){
|
|
if (input$fig_id == "Cinética Grupo"){
|
|
if (input$vacc == "Sí"){
|
|
g<-ggplot(table, aes(as.numeric(as.character(Timepoint)), Volume, color=Group, group=Group))+
|
|
scale_x_continuous(expand = expansion(mult = c(0,0.05)), limits = c(0, (round(max(as.numeric(as.character(table$Timepoint))) / 5)+1)*5))+
|
|
facet_grid(factor(`ID tumor`, labels = c("Vaccination", "Rechallenge"))~., scale="free_y")+
|
|
theme_bw()
|
|
}else{
|
|
g<-ggplot(table, aes(Timepoint, Volume, color=Group, group=Group))+
|
|
theme_bw()
|
|
}
|
|
}
|
|
if (input$fig_id == "Cinética Individual"){
|
|
if (input$vacc == "Sí"){
|
|
g<-ggplot(table, aes(as.numeric(as.character(Timepoint)), Volume, color=Group, group=`ID animal`))+
|
|
scale_x_continuous(expand = expansion(mult = c(0,0.05)), limits = c(0, (round(max(as.numeric(as.character(table$Timepoint))) / 5)+1)*5))+
|
|
facet_grid(factor(`ID tumor`, labels = c("Vaccination", "Rechallenge"))~Group, scale="free_y")+
|
|
theme_bw()
|
|
}else{
|
|
g<-ggplot(table, aes(Timepoint, Volume, color=Group, group=`ID animal`))+
|
|
facet_wrap(.~Group)+
|
|
theme_bw()
|
|
}
|
|
}
|
|
|
|
g<-g+geom_errorbar(stat="summary", width=input$`errorbar-width`)+
|
|
geom_line(stat="summary")+
|
|
geom_point(stat="summary", size=input$`point-size`)+
|
|
labs(x="Days after tumor challenge")+
|
|
scale_y_continuous(expand = expansion(mult = c(0,0.05)))
|
|
|
|
if (input$theme == "BW"){
|
|
g<-g+theme_bw(base_size = input$`font-size`)
|
|
}
|
|
if (input$theme == "Classic"){
|
|
g<-g+theme_classic(base_size = input$`font-size`)
|
|
}
|
|
if (input$theme == "Default"){
|
|
g<-g+theme_gray(base_size = input$`font-size`)
|
|
}
|
|
# g<-g+theme(axis.text.x=element_text(angle=45, hjust=1))
|
|
if (input$legend == F){
|
|
g<-g+guides(color=FALSE, fill=FALSE)
|
|
}
|
|
if (input$colors != ""){
|
|
v_col<-strsplit(input$colors, ",")[[1]]
|
|
g<-g+scale_color_manual(values=v_col)+
|
|
scale_fill_manual(values=v_col)
|
|
}
|
|
if (input$logscale == T){
|
|
g<-g+scale_y_log10()
|
|
}
|
|
}else{
|
|
gg_color_hue <- function(n, l=65) {
|
|
hues <- seq(15, 375, length=n+1)
|
|
hcl(h=hues, l=l, c=100)[1:n]
|
|
}
|
|
if (input$vacc == "Sí"){
|
|
g<-list()
|
|
count<-1
|
|
for (side in c("L","R")){
|
|
tableR<-filter(table, `ID tumor` == side) %>% filter(!is.na(Volume))
|
|
endtime<-dcast(tableR %>% filter(Volume < input$cutoff), Cage+`ID animal`+`ID tumor`+Group~., value.var = "Timepoint", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
|
|
endtime["Dead"]<-dcast(tableR, Cage+`ID animal`+`ID tumor`+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") > input$cutoff
|
|
table_tumor<-endtime
|
|
if (input$colors != ""){
|
|
col<-input$colors
|
|
}else{
|
|
col<-gg_color_hue(length(unique(endtime$Group)))
|
|
}
|
|
g[[count]]<-ggsurvplot(survfit(Surv(table_tumor$end, table_tumor$Dead) ~ table_tumor$Group, data=table_tumor),
|
|
pval = T, pval.method = T,
|
|
title = side,
|
|
# legend.labs = paste(c("< median", ">= median"), "MICA"),
|
|
ggtheme=theme_classic(base_size=input$`font-size`),
|
|
palette = col
|
|
)
|
|
count<-count+1
|
|
}
|
|
g_surv_vacc<-g
|
|
|
|
}else{
|
|
tableR<-table %>% filter(!is.na(Volume))
|
|
endtime<-dcast(tableR %>% filter(Volume < input$cutoff), `ID animal`+Group~., value.var = "Timepoint", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% rename("end"=".")
|
|
endtime["Dead"]<-dcast(tableR, `ID animal`+Group~., value.var = "Volume", fun.aggregate = function(x){max(as.numeric(as.character(x)))}) %>% pull(".") > input$cutoff
|
|
table_tumor<-endtime
|
|
if (input$colors != ""){
|
|
col<-input$colors
|
|
}else{
|
|
col<-gg_color_hue(length(unique(table_tumor$Group)))
|
|
}
|
|
g<-ggsurvplot(survfit(Surv(table_tumor$end, table_tumor$Dead) ~ table_tumor$Group, data=table_tumor),
|
|
pval = T, pval.method = T,
|
|
# legend.labs = paste(c("< median", ">= median"), "MICA"),
|
|
ggtheme=theme_classic(base_size=input$`font-size`),
|
|
palette = col
|
|
)
|
|
}
|
|
}
|
|
|
|
if (input$vacc == "Sí" & input$fig_id == "Survival"){
|
|
dades$plot<<-g_surv_vacc
|
|
arrange_ggsurvplots(g, nrow=2, ncol=1)
|
|
}else{
|
|
dades$plot<<-g
|
|
g
|
|
}
|
|
}
|
|
}, res=72)
|
|
|
|
output$downloadPicture <- downloadHandler(
|
|
filename = function() {
|
|
paste("Figura", ".zip", sep="")
|
|
},
|
|
content = function(file){
|
|
# tempReport <- file.path(tempdir(), "elispots.Rmd")
|
|
# file.copy("elispots.Rmd", tempReport, overwrite = TRUE)
|
|
png("invivo.png", width = input$width, height=input$height, units = "px", res=720)
|
|
if (input$fig_id == "Survival"){
|
|
if (input$vacc == "Sí"){
|
|
arrange_ggsurvplots(dades$plot, nrow=2, ncol=1)
|
|
}else{
|
|
arrange_ggsurvplots(list(dades$plot), ncol=1)
|
|
}
|
|
}else{plot(dades$plot)}
|
|
dev.off()
|
|
if (input$fig_id == "Survival"){
|
|
if (input$vacc == "Sí"){
|
|
invivo.plot<-arrange_ggsurvplots(dades$plot, nrow=2, ncol=1)
|
|
}else{
|
|
invivo.plot<-arrange_ggsurvplots(list(dades$plot), ncol=1)
|
|
}
|
|
}else{invivo.plot<-dades$plot}
|
|
save(invivo.plot, file="invivoplot.RObject")
|
|
zip(file,
|
|
c("invivo.png", "invivoplot.RObject")
|
|
)
|
|
})
|
|
|
|
|
|
}
|
|
|
|
# Run the application
|
|
shinyApp(ui = ui, server = server)
|