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Añadido plot para expresión de genes.

main
Costa 2 years ago
parent
commit
2909f49eec
1 changed files with 54 additions and 11 deletions
  1. +54
    -11
      BDAccess/app.R

+ 54
- 11
BDAccess/app.R

@ -68,12 +68,16 @@ ui <- fluidPage(
sidebarPanel( sidebarPanel(
textInput("sqlquery", label = "sqlquery", value = ""), textInput("sqlquery", label = "sqlquery", value = ""),
uiOutput("PATID"), uiOutput("PATID"),
checkboxInput("cd45_chk", "Purificación CD45")
checkboxInput("sct_sel", "Mostrar filtrados"),
checkboxInput("cd45_chk", "Purificación CD45"),
textInput("genes", label="genes", value = "")
), ),
mainPanel( mainPanel(
tabsetPanel( tabsetPanel(
tabPanel("Table", tableOutput("sc_table")), tabPanel("Table", tableOutput("sc_table")),
tabPanel("Plots", plotOutput("sc_plot"))
tabPanel("Plots",
plotOutput("sc_plot", height = "1000px"),
plotOutput("sc_expr"), height = "600px")
) )
) )
) )
@ -701,20 +705,25 @@ server <- function(input, output) {
}) })
output$sc_table<-renderTable({ output$sc_table<-renderTable({
if (input$sqlquery != ""){
print(input$sqlquery)
sqlQuery(dta, input$sqlquery)
}else{
if (!is.null(input$sc_cod)){
sqlFetch(dta, "CNAG") %>% filter(CODIGO %in% input$sc_cod)
if (input$sct_sel){
if (input$sqlquery != ""){
print(input$sqlquery)
sqlQuery(dta, input$sqlquery)
}else{ }else{
sqlFetch(dta, "CNAG")
if (!is.null(input$sc_cod)){
sqlFetch(dta, "CNAG") %>% filter(CODIGO %in% input$sc_cod)
}else{
sqlFetch(dta, "CNAG")
}
} }
}else{
sqlFetch(dta, "CNAG")
} }
}) })
output$sc_plot <-renderPlot({ output$sc_plot <-renderPlot({
meta<-readRDS(paste0(scRNAseqRoute,"metadata_full_object.rds"))
meta<<-readRDS(paste0(scRNAseqRoute,"metadata_full_object.rds"))
if (input$sqlquery != ""){ if (input$sqlquery != ""){
sc_codigos<-sqlQuery(dta, input$sqlquery) %>% pull(CNAG_NAME) sc_codigos<-sqlQuery(dta, input$sqlquery) %>% pull(CNAG_NAME)
@ -742,8 +751,42 @@ server <- function(input, output) {
spread(predicted.id, N) %>% gather("predicted.id","N",-sample) %>% mutate(N=case_when(is.na(N)~0,TRUE~N)) spread(predicted.id, N) %>% gather("predicted.id","N",-sample) %>% mutate(N=case_when(is.na(N)~0,TRUE~N))
g2<-ggheatmap(meta_perc, "sample","predicted.id", "N", color = "grey50") g2<-ggheatmap(meta_perc, "sample","predicted.id", "N", color = "grey50")
ggpubr::ggarrange(g1,g2, ncol=1, heights = c(0.3, 0.7)) ggpubr::ggarrange(g1,g2, ncol=1, heights = c(0.3, 0.7))
}, height = 1000)
})
output$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 # Run the application

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