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
@@ -198,6 +198,21 @@ output$cluster_plot <- renderPlot({
       guides(color = guide_legend(ncol = 1, override.aes = list(size = 3)))
 })
 
+output$vln_plot <- renderPlot({
+  if (input$dataset=="Neurons"){
+    seu3 <- subset(seu.downsample, cells = colnames(seu.downsample)[seu.downsample$MainType %in% c("Excitatory", "Inhibitory")])
+  }else{
+    seu3 <- seu.downsample
+  }
+  seu3@meta.data[,input$celltype] <- factor(
+    seu3@meta.data[,input$celltype],
+    levels = names(col_cluster[[input$celltype]])
+  )
+  VlnPlot(seu3, features = input$gene, group.by = input$celltype,
+          col = col_cluster[[input$celltype]]) +
+    NoLegend() +
+    labs(x="")
+})
 
 output$gene_plot <- renderPlot({
   if (input$dataset=="Neurons"){
@@ -221,23 +236,6 @@ output$gene_plot <- renderPlot({
     coord_fixed() &
     scale_color_gradientn(colours = c("lightblue3", "lightblue", "white", "red", "red4"), limits=c(0,2), breaks=c(0,2), na.value = "red4")
 })
-
-
-output$vln_plot <- renderPlot({
-  if (input$dataset=="Neurons"){
-    seu3 <- subset(seu.downsample, cells = colnames(seu.downsample)[seu.downsample$MainType %in% c("Excitatory", "Inhibitory")])
-  }else{
-    seu3 <- seu.downsample
-  }
-  seu3@meta.data[,input$celltype] <- factor(
-    seu3@meta.data[,input$celltype],
-    levels = names(col_cluster[[input$celltype]])
-  )
-  VlnPlot(seu3, features = input$gene, group.by = input$celltype,
-          col = col_cluster[[input$celltype]]) +
-    NoLegend() +
-    labs(x="")
-})
 ```