Merge branch 'dev'

BDAccess/app.R

@@ -1,5 +1,9 @@
 library(shiny)
 library(rhandsontable)
+library(tidyverse)
+library(reshape2)
+library(Matrix)
+library(CitFuns)
 library(BDCIT)
 
 print(getwd())
@@ -61,6 +65,23 @@ ui <- fluidPage(
                 h3("Nitrogen"),
                 tableOutput("nitrogen")
             )
+        ),
+        tabPanel("scRNAseq",
+                 sidebarPanel(
+                     textInput("sqlquery", label = "sqlquery", value = ""),
+                     uiOutput("PATID"),
+                     checkboxInput("sct_sel", "Mostrar filtrados"),
+                     checkboxInput("cd45_chk", "Purificación CD45"),
+                     textInput("genes", label="genes", value = "")
+                 ),
+                 mainPanel(
+                     tabsetPanel(
+                         tabPanel("Table", tableOutput("sc_table")),
+                         tabPanel("Plots", 
+                                  plotOutput("sc_plot", height = "1000px"),
+                                  plotOutput("sc_expr"), height = "600px")
+                     )
+                 )
         )
     )
 )
@@ -767,6 +788,95 @@ server <- function(input, output) {
     
     ## scRNAseq
     
+    output$PATID = renderUI({
+        observeEvent(input$goButton, {})
+        sc_cod<-sqlFetch(dta, "CNAG") %>% pull(CODIGO)
+        selectizeInput("sc_cod", "CÓDIGO", sc_cod, multiple = T)
+    })
+    
+    output$sc_table<-renderTable({
+        if (input$sct_sel){
+            if (input$sqlquery != ""){
+                print(input$sqlquery)
+                sqlQuery(dta, input$sqlquery)
+            }else{
+                if (!is.null(input$sc_cod)){
+                    sqlFetch(dta, "CNAG") %>% filter(CODIGO %in% input$sc_cod)
+                }else{
+                    sqlFetch(dta, "CNAG")
+                }
+            }
+        }else{
+            sqlFetch(dta, "CNAG")
+        }
+
+    })
+    
+    output$sc_plot <-renderPlot({
+        meta<<-readRDS(paste0(scRNAseqRoute,"metadata_full_object.rds"))
+        
+        if (input$sqlquery != ""){
+            sc_codigos<-sqlQuery(dta, input$sqlquery) %>% pull(CNAG_NAME)
+        }else{
+            if (!is.null(input$sc_cod)){
+                sc_codigos<-sqlFetch(dta, "CNAG") %>% filter(CODIGO %in% input$sc_cod) %>% pull(CNAG_NAME)
+            }else{
+                sc_codigos<-sqlFetch(dta, "CNAG") %>% pull(CNAG_NAME)
+            }
+        }
+        sc_codigos<-gsub(" _","_", sc_codigos )
+        sc_codigos<-gsub("_ ","_", sc_codigos )
+        sc_codigos<-gsub(" ","_", sc_codigos )
+        
+        meta<-meta %>% mutate(sample2=gsub("_CD45", "", sample)) %>% filter(sample2 %in% sc_codigos)
+        if (isFALSE(input$cd45_chk)){ meta<-meta %>% filter(!grepl("_CD45", sample))}
+        
+        g1<-ggplot(meta, aes(coord_x, coord_y, color=predicted.id))+
+            geom_point(size=0.2)+
+            guides(colour = guide_legend(override.aes = list(size=2)))+
+            theme_bw()+
+            theme(aspect.ratio = 1)
+        
+        meta_perc<-meta %>% group_by(sample, predicted.id) %>% summarise(N=n()) %>% mutate(N=perc(N)) %>% 
+            spread(predicted.id, N) %>% gather("predicted.id","N",-sample) %>% mutate(N=case_when(is.na(N)~0,TRUE~N))
+        g2<-ggheatmap(meta_perc, "sample","predicted.id", "N", color = "grey50")
+        ggpubr::ggarrange(g1,g2, ncol=1, heights = c(0.3, 0.7))
+    })
+
+    output$sc_expr <-renderPlot({
+        if (input$genes != ""){
+            expr<-readRDS(paste0(scRNAseqRoute,"expression_full_object.rds"))
+            genes<-strsplit(input$genes, ",")[[1]]
+
+            if (length(genes) > 1){
+                df.expr<-as.data.frame(as.matrix(expr[genes,]))
+                df.expr["Gene"]<-rownames(df.expr)
+                mdf.expr<-melt(df.expr)
+            }else{
+                df.expr<-as.data.frame(t(as.matrix(expr[genes[1],])))
+                df.expr["Gene"]<-genes[1]
+                mdf.expr<-melt(df.expr)
+            }
+            
+            alldata<-merge(meta, mdf.expr, by.x="barcode", by.y = "variable")
+            
+        }
+        
+        # order <- clustsort(alldata %>% spread(Gene, value) %>% select(predicted.id, all_of(genes)) %>%  
+        #                        group_by(predicted.id) %>% summarise(across(all_of(genes), mean)) %>%  as.data.frame)
+        # 
+        # g1<-ggplot(alldata, aes(predicted.id, value, fill=predicted.id))+
+        #     geom_violin(scale = "width")+
+        #     geom_jitter(width=0.2, size=0.1, alpha=0.3)+
+        #     scale_x_discrete(limits=order$x)+
+        #     guides(fill=F)+
+        #     facet_wrap(.~Gene)+
+        #     theme_bw()+
+        #     theme(axis.text.x = element_text(angle=90, hjust=1, vjust = 0.5))
+        g2<-ggheatmap(alldata, x="predicted.id",y="Gene",value="value", color="grey")+coord_equal()
+        # ggpubr::ggarrange(g1,g2, ncol=1)
+        g2
+    })
 }
 
 # Run the application