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@ -68,12 +68,16 @@ ui <- fluidPage( |
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sidebarPanel( |
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textInput("sqlquery", label = "sqlquery", value = ""), |
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uiOutput("PATID"), |
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checkboxInput("cd45_chk", "Purificación CD45") |
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checkboxInput("sct_sel", "Mostrar filtrados"), |
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checkboxInput("cd45_chk", "Purificación CD45"), |
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textInput("genes", label="genes", value = "") |
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), |
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mainPanel( |
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tabsetPanel( |
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tabPanel("Table", tableOutput("sc_table")), |
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tabPanel("Plots", plotOutput("sc_plot")) |
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tabPanel("Plots", |
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plotOutput("sc_plot", height = "1000px"), |
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plotOutput("sc_expr"), height = "600px") |
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) |
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) |
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) |
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@ -701,20 +705,25 @@ server <- function(input, output) { |
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}) |
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output$sc_table<-renderTable({ |
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if (input$sqlquery != ""){ |
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print(input$sqlquery) |
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sqlQuery(dta, input$sqlquery) |
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}else{ |
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if (!is.null(input$sc_cod)){ |
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sqlFetch(dta, "CNAG") %>% filter(CODIGO %in% input$sc_cod) |
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if (input$sct_sel){ |
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if (input$sqlquery != ""){ |
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print(input$sqlquery) |
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sqlQuery(dta, input$sqlquery) |
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}else{ |
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sqlFetch(dta, "CNAG") |
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if (!is.null(input$sc_cod)){ |
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sqlFetch(dta, "CNAG") %>% filter(CODIGO %in% input$sc_cod) |
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}else{ |
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sqlFetch(dta, "CNAG") |
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} |
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} |
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}else{ |
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sqlFetch(dta, "CNAG") |
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} |
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}) |
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output$sc_plot <-renderPlot({ |
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meta<-readRDS(paste0(scRNAseqRoute,"metadata_full_object.rds")) |
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meta<<-readRDS(paste0(scRNAseqRoute,"metadata_full_object.rds")) |
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if (input$sqlquery != ""){ |
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sc_codigos<-sqlQuery(dta, input$sqlquery) %>% pull(CNAG_NAME) |
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@ -742,8 +751,42 @@ server <- function(input, output) { |
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spread(predicted.id, N) %>% gather("predicted.id","N",-sample) %>% mutate(N=case_when(is.na(N)~0,TRUE~N)) |
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g2<-ggheatmap(meta_perc, "sample","predicted.id", "N", color = "grey50") |
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ggpubr::ggarrange(g1,g2, ncol=1, heights = c(0.3, 0.7)) |
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}, height = 1000) |
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}) |
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output$sc_expr <-renderPlot({ |
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if (input$genes != ""){ |
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expr<-readRDS(paste0(scRNAseqRoute,"expression_full_object.rds")) |
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genes<-strsplit(input$genes, ",")[[1]] |
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if (length(genes) > 1){ |
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df.expr<-as.data.frame(as.matrix(expr[genes,])) |
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df.expr["Gene"]<-rownames(df.expr) |
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mdf.expr<-melt(df.expr) |
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}else{ |
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df.expr<-as.data.frame(t(as.matrix(expr[genes[1],]))) |
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df.expr["Gene"]<-genes[1] |
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mdf.expr<-melt(df.expr) |
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} |
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alldata<-merge(meta, mdf.expr, by.x="barcode", by.y = "variable") |
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} |
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# order <- clustsort(alldata %>% spread(Gene, value) %>% select(predicted.id, all_of(genes)) %>% |
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# group_by(predicted.id) %>% summarise(across(all_of(genes), mean)) %>% as.data.frame) |
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# |
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# g1<-ggplot(alldata, aes(predicted.id, value, fill=predicted.id))+ |
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# geom_violin(scale = "width")+ |
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# geom_jitter(width=0.2, size=0.1, alpha=0.3)+ |
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# scale_x_discrete(limits=order$x)+ |
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# guides(fill=F)+ |
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# facet_wrap(.~Gene)+ |
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# theme_bw()+ |
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# theme(axis.text.x = element_text(angle=90, hjust=1, vjust = 0.5)) |
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g2<-ggheatmap(alldata, x="predicted.id",y="Gene",value="value", color="grey")+coord_equal() |
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# ggpubr::ggarrange(g1,g2, ncol=1) |
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g2 |
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}) |
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} |
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# Run the application |
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