Update PFCapp.qmd

PFCapp.qmd

@@ -86,6 +86,455 @@ Download the raw and processed data from this study.
 </p>
 
 
+```{r}
+#| context: setup
+#| warning: false
+#| message: false
+
+library(ggplot2)
+library(Seurat)
+library(shiny)
+library(rgl)
+library(ggdark)
+library(viridis)
+library(dplyr)
+
+source("R/Palettes.R")
+source('R/includes.R')
+Adult.Ex <- readRDS('data/Adult.Ex.rds')
+sp.PFC <- readRDS('data/sp.PFC.rds')
+sp.PFC$PTi[is.na(sp.PFC$PTi)] <- 0
+sp.PFC$ITi_D[is.na(sp.PFC$ITi_D)] <- 0
+sp.PFC$ITi_V[is.na(sp.PFC$ITi_V)] <- 0
+sp.PFC$ITc[is.na(sp.PFC$ITc)] <- 0
+sp.PFC$Proj_module[which(sp.PFC$Proj_module=="ITi-D")] <- "ITi-M1"
+sp.PFC$Proj_module[which(sp.PFC$Proj_module=="ITi-V")] <- "ITi-M2"
+sp.PFC$Proj_module[which(sp.PFC$Proj_module=="ITc")] <- "ITc-M3"
+colnames(sp.PFC@meta.data)[match(c("ITi_D","ITi_V","ITc"),colnames(sp.PFC@meta.data))] <- c("ITi-M1","ITi-M2","ITc-M3")
+
+
+clean_cells <- colnames(Adult.Ex)[!(
+  (Adult.Ex$Ex_subtype %in% c("CT","NP") & Adult.Ex$BC_num>0) | 
+  (Adult.Ex$sample %in% c("Adult2","Adult3") & Adult.Ex$Ex_subtype=="PT" & Adult.Ex$BC_num>0)
+  )]
+Adult.Ex.clean <- subset(Adult.Ex, cells = clean_cells)
+Adult.Ex.clean$Proj_module[which(Adult.Ex.clean$Proj_module=="ITi-D")] <- "ITi-M1"
+Adult.Ex.clean$Proj_module[which(Adult.Ex.clean$Proj_module=="ITi-V")] <- "ITi-M2"
+Adult.Ex.clean$Proj_module[which(Adult.Ex.clean$Proj_module=="ITc")] <- "ITc-M3"
+colnames(Adult.Ex.clean@meta.data)[match(c("ITi_D_score", "ITi_V_score", "ITc_score", "PTi_score"),colnames(Adult.Ex.clean@meta.data))] <- c("ITi-M1", "ITi-M2","ITc-M3","PTi")
+
+options(rgl.useNULL = TRUE)
+```
+
+
+
+
+
+# scRNAseq {scrolling="true"}
+
+## {.sidebar}
+
+```{r}
+selectInput('cluster', 'Select Cluster', c("SubType_Layer","SubType"))
+```
+
+```{r}
+selectInput('gene', 'Select Gene', rownames(Adult.Ex),
+            selected = "Cux2")
+```
+
+```{r}
+Barcode <- c(
+  "ITi-M1", "ITi-M2", "ITc-M3", "PTi",
+  'VIS-I','SSp-I','CP-I','AUD-I','RSP-I',
+  'BLA-I','ACB-I','ENTl-I','AId-I','ECT-I',
+  'ACB-C','PL-C','ECT-C','ENTl-C',
+  'BLA-C','CP-C','AId-C','RSP-C',
+  'MD-I','RE-I','DR-I','VTA-I','LHA-I','SC-I')
+selectInput('target', 'Select Target', Barcode, selected = "CP-I")
+```
+
+
+## Column
+
+### Row
+
+#### Column
+
+```{r}
+plotOutput('cluster_plot')
+```
+
+#### Column
+
+```{r}
+plotOutput('gene_plot')
+```
+
+### Row
+
+#### Column
+
+```{r}
+plotOutput('target_plot')
+```
+
+#### Column
+
+```{r}
+plotOutput('target_bar_plot')
+```
+
+
+```{r}
+#| context: server
+
+output$cluster_plot <- renderPlot({
+  DimPlot(
+    Adult.Ex,
+    reduction = 'umap',
+    group.by = input$cluster,
+    cols = col_cluster[[input$cluster]],
+    label = T
+  ) +
+    coord_fixed()
+})
+
+output$gene_plot <- renderPlot({
+  FeaturePlot(
+    Adult.Ex,
+    features = input$gene) +
+    coord_fixed()
+})
+
+output$target_plot <- renderPlot({
+  Barcode <- c(
+    "ITi-M1", "ITi-M2","ITc-M3","PTi",
+    'VIS-I','SSp-I','CP-I','AUD-I','RSP-I',
+    'BLA-I','ACB-I','ENTl-I','AId-I','ECT-I',
+    'ACB-C','PL-C','ECT-C','ENTl-C',
+    'BLA-C','CP-C','AId-C','RSP-C',
+    'MD-I','RE-I','DR-I','VTA-I','LHA-I','SC-I'
+    )
+  seu <- Adult.Ex.clean
+  seu@meta.data[,Barcode][is.na(seu@meta.data[,Barcode])] <- 0
+  FeaturePlot(
+    seu, features = input$target, order = T) +
+    coord_fixed()
+})
+
+output$target_bar_plot <- renderPlot({
+  seu <- Adult.Ex.clean
+  if (input$target %in% c("ITi-M1", "ITi-M2","ITc-M3","PTi")){
+    df <- as.data.frame(table(seu@meta.data[,input$cluster][which(seu$Proj_module==input$target)]))
+  }else{
+    df <- as.data.frame(table(seu@meta.data[,input$cluster][which(seu@meta.data[,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$target," cell numbers in different cell type",
+                       sep=""))
+})
+```
+
+
+
+
+
+# Spatial {scrolling="true"}
+
+## {.sidebar}
+
+```{r}
+selectInput('sp_slice', 'Select Slice', unique(sp.PFC$slice), 
+            selected = "IT_slice_10")
+```
+
+```{r}
+selectInput('sp_cluster', 'Select Cluster', c("SubType_Layer","SubType"))
+```
+
+```{r}
+selectInput('sp_gene', 'Select Gene', rownames(sp.PFC),
+            selected = "Cux2")
+```
+
+```{r}
+sp_Barcode <- c(
+  "ITi-M1", "ITi-M2","ITc-M3","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$sp_slice],
+    type = sp.PFC@meta.data[sp.PFC$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$sp_slice),]
+  df$Zscore[df$Zscore<0] <- 0
+  df$Zscore[df$Zscore>3] <- 3
+  df <- df[order(df$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),
+                          limits = c(0,3)) +
+    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({
+  df <- data.frame(
+    X = sp.PFC$ML_new,
+    Y = sp.PFC$DV_new,
+    Zscore = scale(log1p(sp.PFC@meta.data[,input$sp_target]))
+  )
+  df <- df[which(sp.PFC$slice==input$sp_slice),]
+  df$Zscore[df$Zscore<0] <- 0
+  df$Zscore[df$Zscore>3] <- 3
+  df <- df[order(df$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)){
+    if (input$sp_target %in% c("ITi-M1","ITi-M2","ITc-M3","PTi")){
+      df$cellnum[i] <- length(which(seu$slice==slice[i] & seu$Proj_module==input$sp_target))/length(which(seu$slice==slice[i] & seu$BC_num>0))
+    }else{
+      df$cellnum[i] <- length(which(seu$slice==slice[i] & seu@meta.data[,input$sp_target]>0))/length(which(seu$slice==slice[i] & seu$BC_num>0))
+    }
+  }
+  df$x <- c(1:36)
+  p1 <- ggplot(df, aes(x=x, y=cellnum)) +
+    geom_point(alpha=0.5, size=3, color=col_subtype_target[input$sp_target]) +
+    geom_smooth(se = F, linewidth=1.5, color=col_subtype_target[input$sp_target]) +
+    theme_bw() +
+    scale_x_continuous(breaks = seq(0,35,5)) +
+    theme(text = element_text(size=15), 
+          plot.title = element_text(size = 20, hjust = 0.5)) +
+    labs(x='A → P',y='Cell proportion')
+  
+  # DV
+  sp_Barcode <- c("ITi-M1","ITi-M2","ITc-M3", "PTi",
+               'VIS-I','SSp-I','CP-I','AUD-I','RSP-I',
+               'BLA-I','ACB-I','AId-I','ECT-I',
+               'ACB-C','ECT-C','CP-C','AId-C','RSP-C',
+               'LHA-I')
+  seu <- subset(sp.PFC, cells=colnames(sp.PFC)[which(sp.PFC$ABA_hemisphere=="Left")])
+  bc_slice <- seu@meta.data[,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$x <- c(1:36)
+  p2 <- ggplot(df, aes(x=x, y=cellnum)) +
+    geom_point(alpha=0.5, size=3, color=col_subtype_target[input$sp_target]) +
+    geom_smooth(se = F, linewidth=1.5, color=col_subtype_target[input$sp_target]) +
+    theme_bw() +
+    scale_x_continuous(breaks = seq(0,35,5)) +
+    theme(text = element_text(size=15), 
+          plot.title = element_text(size = 20, hjust = 0.5)) +
+    labs(x='V → D',y='Cell proportion')
+  p1/p2
+})
+```
+
+
+
+
+
+
+# 3D
+
+## {.sidebar}
+
+```{r}
+sp_Barcode <- c("ITi-M1","ITi-M2","ITc-M3", "PTi",
+                'VIS-I','SSp-I','CP-I','AUD-I','RSP-I',
+                'BLA-I','ACB-I','AId-I','ECT-I',
+                'ACB-C','ECT-C','CP-C','AId-C','RSP-C',
+                'LHA-I')
+waiter::use_waiter()
+selectInput('subtype_3d', 'Select SubType', sort(unique(sp.PFC$SubType)))
+selectInput('target_3d', 'Select Target', sp_Barcode, selected = "PTi")
+```
+
+
+## Column
+
+```{r}
+rglwidgetOutput('spatial_subtype', width = "100%")
+```
+
+
+```{r}
+#| context: server
+
+observeEvent(input$subtype_3d,{
+  waiter::Waiter$new(id = "spatial_subtype", color="black")$show()
+  output$spatial_subtype <- renderRglwidget({
+    df_plot <- sp.PFC@meta.data[which(sp.PFC$SubType == input$subtype_3d),]
+    
+    open3d()
+    bg3d(color = "black")
+    par3d(userMatrix = rotationMatrix(-pi/6, -1, 1, 0), zoom = 0.6)
+    acr.list <- c("MOs","PL","ORBm","ACAd","ILA","DP","ACAv")
+    
+    for(acr in acr.list){
+      mesh <- mesh3d.allen.annot.from.id(get.id.from.acronym(acr))
+      col <- "lightgray"
+      shade3d(mesh, col = col, material = list(lit=FALSE), alpha = 0.1)
+    }
+    
+    spheres3d(x = df_plot$ML_new, 
+              y = df_plot$DV_new,
+              z = df_plot$AP_new,
+              col = col_subtype_target[input$subtype_3d], radius=0.01, alpha=1)
+    rglwidget()
+  })
+})
+
+
+observeEvent(input$target_3d,{
+  waiter::Waiter$new(id = "spatial_subtype", color="black")$show()
+  output$spatial_subtype <- renderRglwidget({
+    if (input$target_3d %in% c("ITi-M1","ITi-M2","ITc-M3", "PTi")){
+      df_plot <- sp.PFC@meta.data[which(sp.PFC$Proj_module==input$target_3d),]
+    }else{
+      df_plot <- sp.PFC@meta.data[which(sp.PFC@meta.data[,input$target_3d] > 0),]
+    }
+    
+    open3d()
+    bg3d(color = "black")
+    par3d(userMatrix = rotationMatrix(-pi/6, -1, 1, 0), zoom = 0.6)
+    acr.list <- c("MOs","PL","ORBm","ACAd","ILA","DP","ACAv")
+    
+    for(acr in acr.list){
+      mesh <- mesh3d.allen.annot.from.id(get.id.from.acronym(acr))
+      col <- "lightgray"
+      shade3d(mesh, col = col, material = list(lit=FALSE), alpha = 0.1)
+    }
+    
+    spheres3d(x = df_plot$ML_new, 
+              y = df_plot$DV_new,
+              z = df_plot$AP_new,
+              col = col_subtype_target[input$target_3d], radius=0.01, alpha=1)
+    rglwidget()
+  })
+})
+```
+
+
+
+
+# Download
+
+<p style="font-size: 20px; text-align: justify;">
+The raw single cell RNA-seq data are available from GEO (<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE273066">GSE273066</a>).
+</p>
+
+<p style="font-size: 20px; text-align: justify;">
+The raw image data for this study are available via Hugging Face at <a href="https://huggingface.co/TigerZheng/SPIDER-STdata">TigerZheng/SPIDER-STdata</a>. You can download and unzip the .zip file and then use <a href="https://github.com/hms-dbmi/viv">viv</a> to visualize our raw data.
+An example: <a href="https://avivator.gehlenborglab.org/?image_url=https://huggingface.co/TigerZheng/SPIDER-STdata/resolve/main/IT_slice_36_reordered.ome.tiff">IT_slice_36</a>.</p>
+
+<p style="font-size: 20px; text-align: justify;">
+The processed data can be downloaded here:</p>
+
+- All cells data: <a href="https://huggingface.co/spaces/TigerZheng/SPIDER-web/resolve/main/data/all.Adult.rds?download=true">all.Adult.rds</a>
+- Excitatory data: <a href="https://huggingface.co/spaces/TigerZheng/SPIDER-web/resolve/main/data/Adult.Ex.rds?download=true">Adult.Ex.rds</a>
+- Spatial data: <a href="https://huggingface.co/spaces/TigerZheng/SPIDER-web/resolve/main/data/sp.PFC.rds?download=true">sp.PFC.rds</a>