Update PFCdevApp.qmd

PFCdevApp.qmd

@@ -55,7 +55,7 @@ Spatial data
 </p>
 
 <p style="font-size: 20px; text-align: justify;">
-We collected the whole brain stereo-seq datasets of P1 and Adult mice from [(Han et al., Neuron, 2025)](https://doi.org/10.1016/j.neuron.2025.02.015, extracted and analyzed the PFC brain region. Users can browse the following content through the spatial page:
+We collected the whole brain stereo-seq datasets of P1 and Adult mice from [(Han et al., Neuron, 2025)](https://doi.org/10.1016/j.neuron.2025.02.015 , extracted and analyzed the PFC brain region. Users can browse the following content through the spatial page:
 </p>
 
 - Spatial Clustering: Select different cell subtypes to view their spatial distribution
@@ -88,9 +88,9 @@ source("R/Palettes.R")
 
 # scrnaseq
 seu.downsample <- readRDS('data/seu.all.HVGs.rds')
-#seu.downsample$orig.ident[seu.downsample$orig.ident == "P0"] <- "P1"
+seu.downsample$orig.ident[seu.downsample$orig.ident == "P0"] <- "P1"
 seu.downsample$orig.ident <- factor(seu.downsample$orig.ident,
-                                    levels = c("P0","P4","P10","Adult"))
+                                    levels = c("P1","P4","P10","Adult"))
 seu.downsample$SubType <- seu.downsample$SubType_v4
 
 # spatial
@@ -225,26 +225,18 @@ output$gene_plot <- renderPlot({
 
 output$vln_plot <- renderPlot({
   if (input$dataset=="Neurons"){
-    seu <- subset(seu.downsample, cells = colnames(seu.downsample)[seu.downsample$MainType %in% c("Excitatory", "Inhibitory")])
+    seu3 <- subset(seu.downsample, cells = colnames(seu.downsample)[seu.downsample$MainType %in% c("Excitatory", "Inhibitory")])
   }else{
-    seu <- seu.downsample
+    seu3 <- seu.downsample
   }
-  seu@meta.data[,input$celltype] <- factor(
-    seu@meta.data[,input$celltype],
+  seu3@meta.data[,input$celltype] <- factor(
+    seu3@meta.data[,input$celltype],
     levels = names(col_cluster[[input$celltype]])
   )
-  data <- data.frame(
-    Gene = as.numeric(seu@assays$RNA$data[input$gene,]),
-    Cluster = seu@meta.data[,input$celltype]
-  )
-  ggplot(data, aes(x=Cluster, y=Gene, fill=Cluster)) +
-    geom_violin(scale="width", trim = T, adjust=1) +
-    geom_jitter(size=0.1, alpha=0.5) +
-    theme_classic(base_size = 15) +
-    theme(legend.position = "none",
-          plot.title = element_text(face="bold.italic", size = 20, hjust = 0.5)) +
-    scale_fill_manual(values = col_cluster[[input$celltype]]) +
-    labs(x="", y="Expression Level", title = input$gene)
+  VlnPlot(seu3, features = input$gene, group.by = input$celltype,
+          col = col_cluster[[input$celltype]]) +
+    NoLegend() +
+    labs(x="")
 })
 ```