Update PFCapp.qmd

PFCapp.qmd

@@ -86,455 +86,7 @@ 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([email protected])[match(c("ITi_D","ITi_V","ITc"),colnames([email protected]))] <- 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$Proj_module=="ITc")] <- "ITc-M3"
-colnames([email protected])[match(c("ITi_D_score", "ITi_V_score", "ITc_score", "PTi_score"),colnames([email protected]))] <- c("ITi-M1", "ITi-M2","ITc-M3","PTi")
-
-options(rgl.useNULL = TRUE)
-```
-
-
-
-
-
-# scRNAseq {scrolling="true"}
-
-## {.sidebar}
-
-```{r}
-selectInput('cluster', 'Select Cluster', c("SubType_Layer","SubType"))
-```
-
-```{r}
-selectInput('gene', 'Select Gene', rownames(Adult.Ex),
-            selected = "Cux2")
-```
-
-```{r}
-Barcode <- c(
-  "ITi-M1", "ITi-M2", "ITc-M3", "PTi",
-  'VIS-I','SSp-I','CP-I','AUD-I','RSP-I',
-  'BLA-I','ACB-I','ENTl-I','AId-I','ECT-I',
-  'ACB-C','PL-C','ECT-C','ENTl-C',
-  'BLA-C','CP-C','AId-C','RSP-C',
-  'MD-I','RE-I','DR-I','VTA-I','LHA-I','SC-I')
-selectInput('target', 'Select Target', Barcode, selected = "CP-I")
-```
-
-
-## Column
-
-### Row
-
-#### Column
-
-```{r}
-plotOutput('cluster_plot')
-```
-
-#### Column
-
-```{r}
-plotOutput('gene_plot')
-```
-
-### Row
-
-#### Column
-
-```{r}
-plotOutput('target_plot')
-```
-
-#### Column
-
-```{r}
-plotOutput('target_bar_plot')
-```
-
-
-```{r}
-#| context: server
-
-output$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$target_plot <- renderPlot({
-  Barcode <- c(
-    "ITi-M1", "ITi-M2","ITc-M3","PTi",
-    'VIS-I','SSp-I','CP-I','AUD-I','RSP-I',
-    'BLA-I','ACB-I','ENTl-I','AId-I','ECT-I',
-    'ACB-C','PL-C','ECT-C','ENTl-C',
-    'BLA-C','CP-C','AId-C','RSP-C',
-    'MD-I','RE-I','DR-I','VTA-I','LHA-I','SC-I'
-    )
-  seu <- Adult.Ex.clean
-  [email protected][,Barcode][is.na([email protected][,Barcode])] <- 0
-  FeaturePlot(
-    seu, features = input$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([email protected][,input$cluster][which(seu$Proj_module==input$target)]))
-  }else{
-    df <- as.data.frame(table([email protected][,input$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$target," cell numbers in different cell type",
-                       sep=""))
-})
-```
-
-
-
-
-
-# Spatial {scrolling="true"}
-
-## {.sidebar}
-
-```{r}
-selectInput('sp_slice', 'Select Slice', unique(sp.PFC$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$sp_slice],
-    type = [email protected][sp.PFC$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$sp_slice),]
-  df$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([email protected][,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$slice==slice[i] & [email protected][,input$sp_target]>0))/length(which(seu$slice==slice[i] & seu$BC_num>0))
-    }
-  }
-  df$x <- c(1:36)
-  p1 <- ggplot(df, aes(x=x, y=cellnum)) +
-    geom_point(alpha=0.5, size=3, color=col_subtype_target[input$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 <- [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$x <- c(1:36)
-  p2 <- ggplot(df, aes(x=x, y=cellnum)) +
-    geom_point(alpha=0.5, size=3, color=col_subtype_target[input$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 <- [email protected][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$target_3d %in% c("ITi-M1","ITi-M2","ITc-M3", "PTi")){
-      df_plot <- [email protected][which(sp.PFC$Proj_module==input$target_3d),]
-    }else{
-      df_plot <- [email protected][which([email protected][,input$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>