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281 lines
13 KiB
281 lines
13 KiB
library(shiny)
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library(openxlsx)
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library(readxl)
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library(ggplot2)
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library(reshape2)
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library(dplyr)
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library(ggbeeswarm)
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library(magrittr)
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library(flextable)
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source("funcions.R")
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ui <- fluidPage(
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#Navbar
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navbarPage("ELISPOTS",
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tabPanel("Diseño",
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sidebarPanel(
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fileInput(inputId = "file1", label = "Dades", multiple = F),
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selectInput(inputId = "test", "Test Estadístic", selected = "Ttest", choices = c("T-test (adj Holm)","Wilcoxon (adj Holm)")),
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sliderInput(inputId = "umbral_pos", "Mínimo para positivo:", min = 0, max=100, step = 5, value = 10),
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checkboxInput(inputId = "positive", label = "Mostrar positivitat", value = F),
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checkboxInput(inputId = "showstats", label = "Mostrar estadística", value = F),
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downloadButton("downloadData", "Descarregar Informe")
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),
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mainPanel(
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plotOutput("distPlot"),
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uiOutput("flexstats")
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)
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),
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tabPanel("Exportar",
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sidebarPanel(width=2,
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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("boxplot-width", "% Ancho Boxplots", min=0.1, max=1, step=0.1, value=0.7),
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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 = "stats2", label = "Mostrar estadística", 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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dades<-reactiveValues()
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dades$taula<-NULL
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dades$stats<-NULL
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dades$final<-NULL
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dades$maps<-NULL
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dades$plot<-NULL
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observe({
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if (!is.null(input$file1)){
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dades$taula<-read_xlsx(input$file1$datapath)
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}
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})
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output$distPlot <- renderPlot({
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observeEvent(dades$taula, {})
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if (!is.null(dades$taula)){
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ctrl<-"Ctrl+"
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mock<-"Mock"
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table<-dades$taula[,colnames(dades$taula) != "Groups"]
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t_mean<-dcast(melt(table, id="Mice"),Mice~variable, mean, na.rm=T)
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if (length(grep("Mock", colnames(t_mean))) == 1){
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t_substr<-data.frame("Mice"=t_mean[,1],
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as.data.frame(t(apply(t_mean[,2:ncol(table)], 1, function(x) x-x[mock])))
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)
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t_mean_group<-merge(t_mean, unique(dades$taula[c("Mice","Groups")]), id="Mice")
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t_mean_group$Groups<-as.factor(t_mean_group$Groups)
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mock_mean<-dcast(t_mean_group, Groups~., value.var=mock, mean)
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mock_mean[,2]<-mock_mean[,2]*2
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mock_mean[mock_mean$. < input$umbral_pos,2]<-input$umbral_pos
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t_substr<-merge(t_substr, unique(dades$taula[c("Mice","Groups")]), id="Mice")
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t_substr<-t_substr[,c(1, ncol(t_substr), 2:(ncol(t_substr)-1))]
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t_substr<-t_substr[,c("Mice", "Groups", colnames(t_substr)[!colnames(t_substr) %in% c("Mice", "Groups")])]
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t_substr[,3:ncol(t_substr)]<-apply(t_substr[,3:ncol(t_substr)],2, function(x) replace(x, which(x < 0),0))
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colnames(t_substr)<-c("Mice", "Groups", colnames(t_mean)[2:ncol(t_mean)])
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}else{
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t_especifica<-t_mean[,grep("Mock_", colnames(t_mean), invert=T)]
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t_mock<-t_mean[,c(which(colnames(t_mean) == "Mice"),grep("Mock_", colnames(t_mean)))]
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t_temp<-melt(t_especifica, variable.name = "condition", value.name = "spots")
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t_temp["spots_mock"]<-melt(t_mock, variable.name = "condition")[,"value"]
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t_substr<-data.frame(t_temp[c("Mice","condition")], "spots"=t_temp["spots"]-t_temp["spots_mock"])
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t_substr<-dcast(t_substr, Mice~condition)
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t_substr<-merge(t_substr, unique(dades$taula[c("Mice","Groups")]), id="Mice")
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t_substr$Groups<-as.factor(t_substr$Groups)
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}
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t_substr_gp<-t_substr
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t_substr_gp[3:ncol(t_substr)]<-apply(t_substr[3:ncol(t_substr)], 2, function(x) gsub(".",",",x, fixed=T))
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t_substr_gp<-t_substr_gp[order(factor(t_substr_gp$Mice, levels = unique(table$Mice))),]
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doc<-t(t_substr_gp)
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write.xlsx(doc, "data4graphpad.xlsx",rowNames=T)
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t<-melt(t_substr[,!colnames(t_substr) %in% c(ctrl, mock)])
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if (input$showstats == F){
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t_stats<-as.data.frame(matrix(nrow=0, ncol=6))
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colnames(t_stats)<-c("variable", "group1", "group2", "p.adj", "p.signif", "Method")
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t_maps<-list()
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t_maps[["label"]]<-as.data.frame(matrix(nrow = 0, ncol=2))
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colnames(t_maps$label)<-c("x", "y")
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t_maps[["brackets"]]<-as.data.frame(matrix(nrow = 0, ncol=4))
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colnames(t_maps$brackets)<-c("y1", "y2", "x1", "x2")
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dades$stats<<-t_stats
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dades$maps<<-t_maps
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}else{
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if (input$test == "T-test (adj Holm)"){
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t_stats<-multi_stats(t, "value", "variable", "Groups", stat.test = "ttest")
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}
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if (input$test == "Wilcoxon (adj Holm)"){
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t_stats<-multi_stats(t, "value", "variable", "Groups", stat.test = "wilcox")
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}
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dades$stats<<-t_stats
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t_stats<-t_stats %>% filter(p.signif != "ns")
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t_maps<-generate_labstats(t_stats, t, "value", "variable", "Groups")
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dades$maps<<-t_maps
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}
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if (length(unique(dades$taula %>% pull(Groups))) < 2){
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dades$stats<<-null
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}
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c(ctrl, mock)[c(ctrl, mock) %in% colnames(t_substr)]
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dades$final<<-t_substr %>% select(-c(ctrl, mock)[c(ctrl, mock) %in% colnames(t_substr)])
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set.seed(123)
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if (input$positive == T){
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ids=c("Mice", "Groups", c(ctrl, mock)[c(ctrl, mock) %in% colnames(t_substr)])
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validate(
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need(exists("mock_mean"), "No se puede elegir positividad con múltiples mocks")
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)
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if (exists("mock_mean")){
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ggplot(melt(t_substr, id=ids), aes(variable, value))+
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labs(x="", y="Spots/2.5*10^5 cells")+
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# geom_errorbar(stat="summary", position=position_dodge(width=0.9), width=0.5, aes(fill=Groups))+
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geom_hline(data=mock_mean, aes(color=Groups, yintercept = `.`))+
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# geom_bar(stat="summary", position="dodge", color="black", aes(fill=Groups))+
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geom_boxplot(color="black", aes(fill=Groups), alpha=0.4, outlier.alpha = 0)+
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geom_jitter(position=position_jitterdodge(jitter.width = 0.2), shape=21, aes(fill=Groups), size=3)+
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# geom_quasirandom(position = position_quasirandom(), shape=21)+
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scale_x_discrete(limits=colnames(t_substr)[!colnames(t_substr) %in% c("Mice", "Groups", ctrl, mock)])+
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geom_segment(data=t_maps$brackets, aes(x=x1, xend=x2, y=y1, yend=y2), color="black")+
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geom_text(data=t_stats, aes(t_maps$label$x, t_maps$label$y, label=p.signif), color="black")+
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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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}else{
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ids=c("Mice", "Groups", c(ctrl, mock)[c(ctrl, mock) %in% colnames(t_substr)])
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ggplot(melt(t_substr, id=ids), aes(variable, value))+
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labs(x="", y="Spots/2.5*10^5 cells")+
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# geom_errorbar(stat="summary", position=position_dodge(width=0.9), width=0.5, aes(fill=Groups))+
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# geom_bar(stat="summary", position="dodge", color="black", aes(fill=Groups))+
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geom_boxplot(color="black", aes(fill=Groups), alpha=0.4, outlier.alpha = 0)+
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geom_jitter(position=position_jitterdodge(jitter.width = 0.2), shape=21, aes(fill=Groups), size=3)+
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# geom_quasirandom(width=0.2, position=position_dodge(), shape=21)+
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scale_x_discrete(limits=colnames(t_substr)[!colnames(t_substr) %in% c("Mice", "Groups", ctrl, mock)])+
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geom_segment(data=t_maps$brackets, aes(x=x1, xend=x2, y=y1, yend=y2), color="black")+
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geom_text(data=t_stats, aes(t_maps$label$x, t_maps$label$y, label=p.signif), color="black")+
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theme_bw()+
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theme(axis.text.x=element_text(angle=45, hjust=1))
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}
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}
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})
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output$flexstats <- renderUI({
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observeEvent(dades$stats, {})
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t_stats<-dades$stats
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if (!is.null(dades$stats)){
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t_stats %>%
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flextable() %>%
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theme_vanilla() %>%
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fontsize(size=14, part="all") %>%
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padding(padding=10, part="all") %>%
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color(~ p.adj < 0.05, color = "red")%>%
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autofit() %>% htmltools_value()
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}
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})
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output$expPlotUI<- renderUI({
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if (!is.null(dades$final)){
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plotOutput("expPlot", width=paste0(input$width/10,"px"), height = paste0(input$height/10, "px"))
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}
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})
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output$expPlot <- renderPlot({
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observeEvent(dades$final, {})
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if (!is.null(dades$final)){
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print(dades$stats)
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t_substr<-dades$final
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t_stats<-dades$stats %>% filter(p.signif != "ns")
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t_maps<-dades$maps
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ids<-c("Mice", "Groups")
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set.seed(123)
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g<-ggplot(melt(t_substr, id=ids), aes(variable, value))+
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labs(x="", y="Spots/2.5*10^5 cells")+
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geom_boxplot(color="black", aes(fill=Groups), alpha=0.4, outlier.alpha = 0, position=position_dodge(width=0.8),width=input$`boxplot-width`)+
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geom_jitter(position=position_jitterdodge(jitter.width = 0.2), shape=21, aes(fill=Groups), size=input$`point-size`)+
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scale_x_discrete(limits=colnames(t_substr)[!colnames(t_substr) %in% c("Mice", "Groups")])
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if (input$theme == "BW"){
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g<-g+theme_bw(base_size = input$`font-size`)
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}
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if (input$theme == "Classic"){
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g<-g+theme_classic(base_size = input$`font-size`)
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}
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if (input$theme == "Default"){
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g<-g+theme_gray(base_size = input$`font-size`)
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}
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g<-g+theme(axis.text.x=element_text(angle=45, hjust=1))
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if (input$stats2 == T){
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g<-g+geom_segment(data=t_maps$brackets, aes(x=x1, xend=x2, y=y1, yend=y2), color="black")+
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geom_text(data=t_stats, aes(t_maps$label$x, t_maps$label$y, label=p.signif), color="black")
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}
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if (input$legend == F){
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g<-g+guides(color=FALSE, fill=FALSE)
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}
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if (input$colors != ""){
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v_col<-strsplit(input$colors, ",")[[1]]
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g<-g+scale_color_manual(values=v_col)+
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scale_fill_manual(values=v_col)
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}
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dades$plot<<-g
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g
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}
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}, res=72)
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output$downloadData <- downloadHandler(
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filename = function() {
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paste("elispot", ".zip", sep="")
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},
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content = function(file){
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print(file)
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# tempReport <- file.path(tempdir(), "elispots.Rmd")
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# file.copy("elispots.Rmd", tempReport, overwrite = TRUE)
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params=list(file=input$file1$datapath, positive=input$positive, showstats=input$showstats, test=input$test, umbral_pos=input$umbral_pos)
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rmarkdown::render("elispots.Rmd", output_file="Results_elispot.html", params=params, envir = new.env(parent = globalenv()))
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zip(file,
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c("Results_elispot.html","data4graphpad.xlsx")
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)
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},
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contentType="application/zip"
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)
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output$downloadPicture <- downloadHandler(
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filename = function() {
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paste("Figura", ".zip", sep="")
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},
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content = function(file){
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print(file)
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# tempReport <- file.path(tempdir(), "elispots.Rmd")
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# file.copy("elispots.Rmd", tempReport, overwrite = TRUE)
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png("plot.png", width = input$width, height=input$height, units = "px", res=720)
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plot(dades$plot)
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dev.off()
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save(dades$plot, list="plot", file="plot.RObject")
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zip(file,
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c("plot.png")
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)
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}
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)
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}
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# Run the application
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shinyApp(ui = ui, server = server)
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