Update PFCapp.qmd

PFCapp.qmd

@@ -38,19 +38,53 @@ Medial prefrontal cortex (mPFC) is the high-level center of brain cognitive func
 Interactively exploring the data
 </p>
 
-<a style="font-size: 20px; font-weight: bold; text-align: left;">
+<p style="font-size: 20px; font-weight: bold; text-align: left;">
 scRNAseq
-</a>
+</p>
+
+<p style="font-size: 20px; text-align: justify;">
+Our scRNAseq dataset sequenced the PFC of 3 mice. It contains the transcriptome of mouse PFC and the projectome information of 24 PFC targets. Users can browse the following content through the scRNA-seq tab:
+</p>
 
-- Cell cluster UMAP
-- Feature plot in UMAP
+- Cells in the UMAP can be clustered differently by selecting different classification in Metadata
+- Select to view different genes expression in the UMAP
+- Select to view different PFC targets expression in the UMAP
+- View PFC targets cell numbers in different cell type
 
-<a style="font-size: 20px; font-weight: bold; text-align: left;">
+
+
+<p style="font-size: 20px; font-weight: bold; text-align: left;">
 Spatial data
-</a>
+</p>
+
+<p style="font-size: 20px; text-align: justify;">
+Our spatial dataset sequenced 36 slices of mouse PFC. It contains 32 genes and 15 targets information of mouse PFC. Users can browse the following content through the spatial tab:
+</p>
+
+- Select to view different cell types in spatial
+- Select to view different gene expression in spatial
+- Select to view different PFC targets expression in spatial
+- Select to view different PFC targets distribution in anterior-posterior and ventralis-dorsalis axes
+
+
+
+<p style="font-size: 20px; font-weight: bold; text-align: left;">
+3D
+</p>
+
+<p style="font-size: 20px; text-align: justify;">
+3D interactive visualization of mouse PFC.
+</p>
 
 
 
+<p style="font-size: 20px; font-weight: bold; text-align: left;">
+raw image
+</p>
+
+<p style="font-size: 20px; text-align: justify;">
+visualization of raw images.
+</p>
 
 
 ```{r}
@@ -63,6 +97,9 @@ library(Seurat)
 #library(scCustomize)
 library(shiny)
 library(rgl)
+library(ggdark)
+library(viridis)
+library(tidyverse)
 #library(wholebrain)
 #attach(loadNamespace('wholebrain'), warn.conflicts = FALSE)
 source("R/Palettes.R")
@@ -74,24 +111,62 @@ options(rgl.useNULL = TRUE)
 
 
 
-# scRNAseq
 
-## Row
 
-### Column
+# scRNAseq {scrolling="true"}
+
+## {.sidebar}
 
 ```{r}
 selectInput('cluster', 'Select Cluster', c("SubType_Layer","SubType"))
+```
+
+```{r}
+selectInput('gene', 'Select Gene', rownames(Adult.Ex))
+```
+
+```{r}
+Barcode <- c(
+  "ITi_D_score", "ITi_V_score", "ITc_score", "PTi_score",
+  'VIS-I','SSp-I','CP-I','AUD-I','RSP-I',
+  'BLA-I','ACB-I','ENTl-I','AId-I','ECT-I',
+  'ACB-C','PL-C','ECT-C','ENTl-C',
+  'BLA-C','CP-C','AId-C','RSP-C',
+  'MD-I','RE-I','DR-I','VTA-I','LHA-I','SC-I')
+selectInput('target', 'Select Target', Barcode)
+```
+
+
+## Column
+
+### Row
+
+#### Column
+
+```{r}
 plotOutput('cluster_plot')
 ```
 
-### Column
+#### Column
 
 ```{r}
-selectInput('gene', 'Select Gene', rownames(Adult.Ex))
 plotOutput('gene_plot')
 ```
 
+### Row
+
+#### Column
+
+```{r}
+plotOutput('target_plot')
+```
+
+#### Column
+
+```{r}
+plotOutput('target_bar_plot')
+```
+
 
 ```{r}
 #| context: server
@@ -113,29 +188,238 @@ output$gene_plot <- renderPlot({
     features = input$gene) +
     coord_fixed()
 })
+
+output$target_plot <- renderPlot({
+  Barcode <- c(
+  "ITi_D_score", "ITi_V_score", "ITc_score", "PTi_score",
+  'VIS-I','SSp-I','CP-I','AUD-I','RSP-I',
+  'BLA-I','ACB-I','ENTl-I','AId-I','ECT-I',
+  'ACB-C','PL-C','ECT-C','ENTl-C',
+  'BLA-C','CP-C','AId-C','RSP-C',
+  'MD-I','RE-I','DR-I','VTA-I','LHA-I','SC-I')
+  
+  seu <- Adult.Ex
+  [email protected][,Barcode][is.na([email protected][,Barcode])] <- 0
+  FeaturePlot(
+    seu, features = input$target, order = T) +
+    coord_fixed()
+})
+
+output$target_bar_plot <- renderPlot({
+  df <- as.data.frame(table([email protected][,input$cluster][which(
+    [email protected][,input$target]>0)]))
+  colnames(df) <- c("Celltypes","Cellnum")
+  
+  ggplot(df, aes(x=Celltypes, y=Cellnum, fill=Celltypes)) +
+    geom_col() +
+    scale_fill_manual(values = col_cluster[[input$cluster]]) +
+    theme_classic() +
+    theme(axis.text.x = element_text(angle = 25, hjust = 1),
+          plot.title = element_text(hjust = 0.5)) +
+    labs(title = paste("PFC → ",input$target," cell numbers in different cell type",
+                       sep=""))
+})
 ```
 
 
 
 
 
+# Spatial {scrolling="true"}
 
-# Spatial
+## {.sidebar}
 
-```{=html}
-<iframe src="https://avivator.gehlenborglab.org/?image_url=https://huggingface.co/YAYUhuang/Imagesave/resolve/main/PT-2-C2-C.ome.tiff" title="PFC" height="800" width="100%"></iframe>
+```{r}
+selectInput('sp_slice', 'Select Slice', unique(sp.PFC$slice))
 ```
 
+```{r}
+selectInput('sp_cluster', 'Select Cluster', c("SubType_Layer","SubType"))
+```
 
+```{r}
+selectInput('sp_gene', 'Select Gene', rownames(sp.PFC))
+```
+
+```{r}
+sp_Barcode <- c(
+  "ITi_D", "ITi_V", "ITc", "PTi",
+  'VIS-I','SSp-I','CP-I','AUD-I','RSP-I',
+  'BLA-I','ACB-I','AId-I','ECT-I',
+  'ACB-C','ECT-C','CP-C','AId-C','RSP-C',
+  'LHA-I')
+selectInput('sp_target', 'Select Target', sp_Barcode)
+```
+
+
+## Column
+
+### Row
+
+#### Column
+
+```{r}
+#| fig-width: 10
+
+plotOutput('sp_cluster_plot')
+```
+
+#### Column
+
+```{r}
+#| fig-width: 10
+
+plotOutput('sp_gene_plot')
+```
+
+### Row
+
+#### Column
+
+```{r}
+#| fig-width: 10
+
+plotOutput('sp_target_plot')
+```
+
+#### Column
+
+```{r}
+#| fig-width: 10
+
+plotOutput('sp_target_line_plot')
+```
+
+```{r}
+#| context: server
+
+output$sp_cluster_plot <- renderPlot({
+  df <- data.frame(
+    x = sp.PFC$ML_new[sp.PFC$slice==input$sp_slice],
+    y = sp.PFC$DV_new[sp.PFC$slice==input$sp_slice],
+    type = [email protected][sp.PFC$slice==input$sp_slice, input$sp_cluster]
+  )
+  ggplot(df, aes(x=x, y=y, color=type)) +
+    geom_point(size=1) +
+    scale_color_manual(values = col_cluster[[input$sp_cluster]]) +
+    labs(title = paste(input$slice,'cell types in spatial')) +
+    guides(color=guide_legend(nrow = 2, byrow = TRUE, reverse = T,
+                              override.aes = list(size=2))) +
+    coord_fixed() +
+    ggdark::dark_theme_void() +
+    theme(plot.title = element_text(size = 20, hjust = 0.5),
+          legend.position = 'bottom', legend.title=element_blank(),
+          legend.text = element_text(size=10))
+})
+
+output$sp_gene_plot <- renderPlot({
+  df <- data.frame(
+    X = sp.PFC$ML_new,
+    Y = sp.PFC$DV_new,
+    Zscore = scale(log1p(sp.PFC@assays$RNA@counts[input$sp_gene,]))
+    )
+  df <- df[which(sp.PFC$slice==input$sp_slice),]
+  df <- df[order(df$Zscore),]
+  ggplot(df,aes(x=X,y=Y)) +
+    geom_point(aes(colour=Zscore), size=1) +
+    scale_color_gradientn(colours = viridis(n = 256, option = "D", direction = 1)) +
+    ggdark::dark_theme_void() +
+    labs(title = input$sp_gene) +
+    theme(plot.title = element_text(size = 20, hjust = 0.5),
+          legend.position = 'bottom') +
+    coord_fixed()
+})
+
+output$sp_target_plot <- renderPlot({
+  seu <- sp.PFC
+  [email protected][,c("PTi","ITi_D","ITi_V","ITc")][is.na([email protected][,c("PTi","ITi_D","ITi_V","ITc")])] <- 0
+  df <- data.frame(
+    X = sp.PFC$ML_new,
+    Y = sp.PFC$DV_new,
+    Zscore = scale(log1p([email protected][,input$sp_target]))
+  )
+  df <- df[which(sp.PFC$slice==input$sp_slice),]
+  df <- df[order(df$Zscore),]
+  ggplot(df, aes(x=X,y=Y)) +
+    geom_point(aes(colour=Zscore), size=1) +
+    scale_color_gradientn(colours = viridis(n = 256, option = "E", direction = 1)) +
+    ggdark::dark_theme_void() +
+    labs(title = input$sp_target) +
+    theme(plot.title = element_text(size = 20, hjust = 0.5),
+          legend.position = 'bottom') +
+    coord_fixed()
+})
+
+output$sp_target_line_plot <- renderPlot({
+  # AP
+  seu <- subset(sp.PFC, cells=colnames(sp.PFC)[which(sp.PFC$ABA_hemisphere=="Left")])
+  slice <- unique(seu$slice)
+  df <- data.frame('slice'=slice)
+  for (i in 1:length(slice)){
+    df$cellnum[i] <- length(which(seu$slice==slice[i] &
+                                  [email protected][,input$sp_target]>0))/
+      length(which(seu$slice==slice[i] & seu$BC_num>0))
+  }
+  df$x <- c(1:36)
+  p1 <- ggplot(df, aes(x=x, y=cellnum)) +
+    geom_point(alpha=0.5, size=3, color=col_Barcode[input$sp_target]) +
+    geom_smooth(se = F, linewidth=1.5, color=col_Barcode[input$sp_target]) +
+    theme_bw() +
+    scale_x_continuous(breaks = seq(0,35,5)) +
+    theme(text = element_text(size=15), 
+          plot.title = element_text(size = 20, hjust = 0.5)) +
+    labs(x='A → P',y='Cell proportion')
+  
+  # DV
+  sp_Barcode <- c("ITi_D", "ITi_V", "ITc", "PTi",
+               'VIS-I','SSp-I','CP-I','AUD-I','RSP-I',
+               'BLA-I','ACB-I','AId-I','ECT-I',
+               'ACB-C','ECT-C','CP-C','AId-C','RSP-C',
+               'LHA-I')
+  seu <- subset(sp.PFC, cells=colnames(sp.PFC)[which(sp.PFC$ABA_hemisphere=="Left")])
+  bc_slice <- [email protected][,c(sp_Barcode, 'Y','BC_num')]
+  bc_slice <- 
+    bc_slice |>
+    mutate(bin = cut(Y, breaks = 36))
+  bin <- sort(unique(bc_slice$bin))
+  bc_slice$bin_index <- match(bc_slice$bin, bin)
+  df <- data.frame('bin_index'=c(1:36))
+  for (i in 1:36){
+    df$cellnum[i] <- length(which(bc_slice$bin_index==i &
+                                  bc_slice[,input$sp_target]>0))/
+    length(which(bc_slice$bin_index==i & bc_slice$BC_num>0))
+  }
+  df$x <- c(1:36)
+  p2 <- ggplot(df, aes(x=x, y=cellnum)) +
+    geom_point(alpha=0.5, size=3, color=col_Barcode[input$sp_target]) +
+    geom_smooth(se = F, linewidth=1.5, color=col_Barcode[input$sp_target]) +
+    theme_bw() +
+    scale_x_continuous(breaks = seq(0,35,5)) +
+    theme(text = element_text(size=15), 
+          plot.title = element_text(size = 20, hjust = 0.5)) +
+    labs(x='D → V',y='Cell proportion') +
+    xlim(36, 0)
+  p1/p2
+})
+```
 
-# 3D
 
-## Row
 
-### Column
+
+
+
+# 3D
+
+## {.sidebar}
 
 ```{r}
 selectInput('subtype', 'Select SubType', sort(unique(sp.PFC$SubType)))
+```
+
+
+## Column
+
+```{r}
 rglwidgetOutput('spatial_subtype', width = "100%")
 ```
 
@@ -173,3 +457,14 @@ output$spatial_subtype <- renderRglwidget({
 
 
 
+
+# raw image
+
+```{=html}
+<iframe src="https://avivator.gehlenborglab.org/?image_url=https://huggingface.co/YAYUhuang/Imagesave/resolve/main/PT-2-C2-C.ome.tiff?rel=0&amp;autoplay=1" title="PFC" height="800" width="100%"></iframe>
+```
+
+
+
+
+