completar análisis

invivos/app.R

@@ -6,6 +6,7 @@ library(dplyr)
 library(car)
 library(ggbeeswarm)
 library(gtools)
+library(gridExtra)
 source("../../funcions.R")
 
 # Define UI for application
@@ -17,21 +18,42 @@ ui <- fluidPage(
                 sidebarPanel(
                   fileInput(inputId = "file_sizes", label = "Hoja de tamaños", multiple = F),
                   selectInput(inputId = "measure_sys", "Sistema de medida", selected = "L-W-D", choices = c("L-W-D","Min-Max","Absorbance")),
-                  sliderInput("ncages", "Cajas", min=1, max=10, value=1),
-                  sliderInput("iterations", "Iteraciones", min=100, max=2000, step=100, value=100),
+                  uiOutput('ncages'),
+                  uiOutput('lowcut'),
+                  uiOutput('upcut'),
+                  uiOutput('goButton'),
+                  uiOutput('iterations'),
                   downloadButton("downloadData", "Descargar Excel")
                   ),
                 mainPanel(
+                  plotOutput("firstPlot"),
                   plotOutput("distPlot") 
                 )
               ),
-              tabPanel("Análisis")
+              tabPanel("Análisis",
+                       sidebarPanel(
+                         fileInput(inputId = "file_analy", label = "Hoja de análisis", multiple = F),
+                         selectInput(inputId = "vacc", "Experimento de Vacunación", selected = "No", choices = c("Sí","No")),
+                         checkboxInput("filter_stats","Filtrar Estadística")
+                       ),
+                       mainPanel(
+                         h3('Ratones'),
+                         tableOutput('ntable'),
+                         h3('Cinéticas'),
+                         plotOutput('cin_group'),
+                         plotOutput('cin_indiv'),
+                         h3('Estadística'),
+                         verbatimTextOutput('stats'),
+                         tableOutput('tab_stats')
+                       ))
               )
    
    )
 
 # Define server logic required to draw a histogram
 server <- function(input, output) {
+  
+  # Diseño
   dades<-reactiveValues()
   dades$taula<-NULL
   dades$groups<-NULL
@@ -42,12 +64,42 @@ server <- function(input, output) {
       dades$groups<-read.xlsx(input$file_sizes$datapath, sheet = 2)[,1]
     }
   })
-  output$distPlot <- renderPlot({
+  output$firstPlot <- renderPlot({
     observeEvent(dades$taula, {})
     if (!is.null(dades$taula)){
+      ggplot(dades$taula, aes(x="1", y=Volumen))+geom_quasirandom(width=0.2)
+    }
+  })
+  
+  output$iterations<-renderUI({
+    if (!is.null(dades$taula)){
+      sliderInput("iterations", "Iteraciones", min=100, max=2000, step=100, value=100) 
+    }
+  })
+  output$lowcut<-renderUI({
+    if (!is.null(dades$taula)){
+      sliderInput("lowcut", "Corte inferior", min=0, max=1000, step=5, value=0)
+    }
+  })
+  output$upcut<-renderUI({
+    if (!is.null(dades$taula)){
+      sliderInput("upcut", "Corte superior", min=0, max=1000, step=5, value=400)
+    }
+  })
+  output$goButton<-renderUI({
+    if (!is.null(dades$taula)){
+      actionButton("goButton", "Selecciona")
+    }
+  })
+  output$ncages<-renderUI({
+    if (is.null(dades$taula)){
+      sliderInput("ncages", "Cajas", min=1, max=10, value=1)
+    }
+  })
+  grafic<-eventReactive(input$goButton,{
     df<-dades$taula
-    up_cuttof<-400
-    low_cuttof<-50
+    up_cuttof<-input$upcut
+    low_cuttof<-input$lowcut
     df<-df[df$Volumen < up_cuttof & df$Volumen > low_cuttof,]
     df["Mouse"]<-gsub("[a-zA-Z]", "", df$MouseID)
     
@@ -88,6 +140,11 @@ server <- function(input, output) {
       geom_jitter(width=0.25)+
       geom_point(stat="summary", color="blue", size=3)+
       lims(y=c(0,max(df_def$Volumen)+10))
+  })
+  output$distPlot <- renderPlot({
+    observeEvent(dades$taula, {})
+    if (!is.null(dades$taula)){
+      grafic()
     }
   })
   
@@ -144,7 +201,126 @@ server <- function(input, output) {
       write.xlsx(dtemplate,file)
     }
   )
-   
+  
+  # Análisis
+  analysis<-reactiveValues()
+  analysis$taula<-NULL
+  analysis$taula_def<-NULL
+  observe({
+    if (!is.null(input$file_analy)){ 
+      analysis$taula<-read.xlsx(input$file_analy$datapath, sheet = 1)
+    }
+  })
+  output$ntable<-renderTable({
+    if (!is.null(input$file_analy)){
+      observeEvent(analysis$taula, {})
+      stattest<-"dunn"
+      oneside<-""
+      cutoff<-750
+      
+      table<-analysis$taula
+      table<- table %>% add_column("0"=0, .before="12")
+      table[table$ID.tumor == "R","0"]<-NA
+      table<-melt(table, id=colnames(table)[1:6], variable.name = "Timepoint")
+      if ("DPV" %in% colnames(table)){
+        table<-dcast(table, Cage+ID.animal+ID.tumor+Group+Timepoint~DPV, value.var = "value")
+        table$Major<-table$Major/1000
+        table$Minor<-table$Minor/1000
+        table["Volume"]<-((table$Major*table$Minor*table$Minor)*(pi/6))*1000
+      }
+      if ("TS" %in% colnames(table)){
+        table<-dcast(table, Cage+ID.animal+ID.tumor+Group+Timepoint~TS, value.var = "value")
+        table["Volume"]<-table$`TS-Deep`*table$`TS-Length`*table$`TS-Width`*pi/6
+      }
+      table<-table %>% filter(!is.na(Group))
+      analysis$taula_def<-table
+
+      table_plot<-dcast(dcast(table %>% filter(!is.na(Volume)), Cage+`ID.animal`+Group+Timepoint~., value.var = "Volume", fun.aggregate = mean), Group~Timepoint)
+      table_plot
+    }
+  })
+  output$cin_group<-renderPlot({
+    if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
+      observeEvent(analysis$taula_def, {})
+      table<-analysis$taula_def
+      ggplot(table, aes(as.numeric(as.character(Timepoint)), Volume, color=Group, group=Group))+
+        geom_errorbar(stat="summary", width=0.05)+
+        geom_line(stat="summary")+
+        geom_point(stat="summary")+
+        facet_grid(factor(ID.tumor, labels = c("Vaccination", "Rechallenge"))~., scale="free_y")+
+        labs(x="Days after tumor challenge")+
+        scale_y_continuous(expand = expansion(mult = c(0,0.05)))+
+        scale_x_continuous(expand = expansion(mult = c(0,0.05)), limits = c(0, (round(max(as.numeric(as.character(table$Timepoint))) / 5)+1)*5))+
+        theme_bw()
+    }
+  })
+  output$cin_indiv<-renderPlot({
+    if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
+      observeEvent(analysis$taula_def, {})
+      table<-analysis$taula_def
+      ggplot(table, aes(as.numeric(as.character(Timepoint)), Volume, color=Group, group=paste0(Cage,ID.animal)))+
+        # geom_errorbar(stat="summary", width=0.05)+
+        geom_line()+
+        geom_point()+
+        scale_y_continuous(expand = expansion(mult = c(0,0.05)))+
+        scale_x_continuous(expand = expansion(mult = c(0,0.05)), limits = c(0, (round(max(as.numeric(as.character(table$Timepoint))) / 5)+1)*5))+
+        facet_grid(factor(ID.tumor, labels = c("Vaccination", "Rechallenge"))~Group, scale="free_y")+
+        labs(x="Days after tumor challenge")+
+        theme_bw()
+    }
+  })
+  output$stats<-renderPrint({
+    stattest<-"dunn"
+    oneside<-""
+    cutoff<-750
+    if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
+      observeEvent(analysis$taula_def, {})
+      table<-analysis$taula_def
+      if (oneside != ""){
+        tableR<-filter(table, ID.tumor == rechallenge) %>% filter(!is.na(Volume))
+        summary(aov(Volume~Group+Timepoint+Error(ID.animal), data=table[table$ID.tumor == oneside,]))
+      }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$tab_stats<-renderTable({
+    stattest<-"dunn"
+    oneside<-""
+    cutoff<-750
+    if (!is.null(input$file_analy) & !is.null(analysis$taula_def)){
+      table<-analysis$taula_def
+      table_stats<-list()
+      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 
+      }
+    }
+  })
 }
 
 # Run the application