Astrocytes - cluster analysis

samples.df <- readRDS(file.path(dir.objects, "metadata.rds"))
samples.df

so.astro <- readRDS(file.path(dir.objects, "so.astro.rds"))

#[email protected]$ident <- as.factor([email protected]$integrated_snn_res.0.2)

Idents(so.astro) <- "integrated_snn_res.0.2"

# Prepare a vector of colors for the clusters
clusters <- levels(Idents(so.astro)) %>% as.numeric()
cluster.cols <- muscat:::.cluster_colors[seq_along(clusters)]
names(cluster.cols) <- clusters

clusters

 [1] 0 1 2 3 4 5 6 7 8 9

astro.markers <- readRDS(file.path(dir.objects, "astro.markers.v1.rds")) %>% 
  remove_rownames() %>% 
  mutate(gene.name = word(gene, 2, sep = "\\."))
head(astro.markers)

DimPlot(
  so.astro,
  reduction = "tsne",
  group.by = "integrated_snn_res.0.2",
  split.by = "group_id"
) +
  scale_color_manual(values = cluster.cols)

astro.markers.subset <- astro.markers %>% 
  group_by(cluster) %>%
  slice_max(n = 15, order_by = avg_log2FC)
astro.markers.subset

# Expression matrix
cells_to_clusters <- data.frame(cluster = so.astro$integrated_snn_res.0.2) %>% 
  rownames_to_column("cell")

geneset <- unique(astro.markers.subset$gene)

mat.scale.ours_data <-
  so.astro@assays$integrated@scale.data[geneset, cells_to_clusters$cell]

cluster_indices <-
  split(cells_to_clusters$cell, cells_to_clusters$cluster)

cluster_means <- sapply(cluster_indices, function(indices) {
  Matrix::rowMeans(mat.scale.ours_data[, indices, drop = FALSE])
})

rownames(cluster_means) <-
  word(rownames(cluster_means), 2, sep = "\\.")

# Gene cluster color bar annotation
# by cluster order
genes.clusters.cols <- astro.markers.subset %>%
  select(gene, cluster) %>%
  mutate(gene.full = gene,
         gene = word(gene.full, 2, sep = "\\.")) %>%
  group_by(gene) %>%
  slice_min(n = 1, order_by = cluster) %>%
  ungroup() %>%
  arrange(cluster) %>%
  left_join(., enframe(cluster.cols, name = "cluster", value = "colors"))
rownames(genes.clusters.cols) <- genes.clusters.cols$gene
genes.clusters.cols <-
  genes.clusters.cols[rownames(cluster_means),]

row.anno.genes <-
  rowAnnotation(
    "Cluster" = genes.clusters.cols$cluster,
    col = list("Cluster" = cluster.cols),
    show_annotation_name = FALSE
  )

# Define the range and breaks
breaks <-
  seq(0, 1, length.out = 11)  # This creates 11 points between 0 and 1

colors <- plasma(11)

# Create a color function that maps the range [0, 1] to the viridis palette
color_mapping <- colorRamp2(breaks, colors)

# Use the adjusted col argument
Heatmap(
  cluster_means,
  name = "Normal. expression",
  col = color_mapping,
  cluster_rows = FALSE,
  show_row_dend = FALSE,
  cluster_columns = FALSE,
  column_names_side = "bottom",
  row_names_side = "left",
  column_names_rot = 0,
  column_names_centered = TRUE,
  row_names_gp = gpar(fontsize = 8),
  left_annotation = row.anno.genes
)

# Specify the directory containing the H5 file and the image data
dir.Spatial_H5 <- file.path(wd, "GSM5026144")

# Load the data into a Seurat object
GSM5026144_S7_Seurat <- Load10X_Spatial(
  data.dir = dir.Spatial_H5,
  filename = "GSM5026144_S7_filtered_feature_bc_matrix.h5",
  assay = "Spatial",
  slice = "slice1",
  filter.matrix = TRUE,
  to.upper = FALSE,
  image = NULL
)

GSM5026144_S7_Seurat <-
  SCTransform(
    object = GSM5026144_S7_Seurat,
    assay = 'Spatial',
    return.only.var.genes = F,
    verbose = FALSE
  )
GSM5026144_S7_Seurat

An object of class Seurat 
50573 features across 2511 samples within 2 assays 
Active assay: SCT (18288 features, 3000 variable features)
 3 layers present: counts, data, scale.data
 1 other assay present: Spatial
 1 image present: slice1

astrocyte_markers <- c("Aldh1l1", "Slc1a2", "S100b", "Gfap", "Myoc")

cluster_0.module <- list(c("Gm42418", "Arhgap31", "Kcnq1ot1", "Rmst", "Cit", "Gli1", "Ttll3", "Neat1", "Gm26917"))
cluster_1.module <- list(c("Mfge8", "Igfbp2", "Btbd17"))
cluster_2.module <- list(c("Dbi", "Agt", "Nnat", "Ppia", "Tmsb4x", "Scrg1", "Mt3", "Gstm1", "Nkain4", "S100a1"))
cluster_3.module <- list(c("Thrsp", "Actb", "Chchd10", "Hint1", "Mrpl41", "Mpc1", "Camk2n1", "Ndufc2", "Nupr1", "Igfbp2"))
cluster_4.module <- list(c("Lcn2", "Ifitm3", "Timp1", "B2m", "Vim", "Gfap", "Psmb8", "Bst2"))
cluster_5.module <- list(c("Crym", "Prss56", "Slc43a3", "Mgst1", "Slc1a5", "Tuba1a", "Itm2b", "Chil1", "Ptn"))
cluster_6.module <- list(c("Thbs4", "Igfbp5", "Fxyd6", "Gsn", "Fxyd1", "Cd9", "Meg3"))
cluster_7.module <- list(c("Itih3", "Sparc", "Nkx6-2", "Etnppl", "Gria1", "Spon1", "5031439G07Rik"))
cluster_8.module <- list(c("Igtp", "Gm4951", "Ifit3", "Iigp1", "Irgm1", "Tap1", "Gbp2", "Ifit1", "Isg15", "Cxcl10"))
cluster_9.module <- list(c("Zic1", "Slc38a1", "Col1a2", "Kcnk2", "Luzp2", "Mgll", "Zic4", "Hopx"))

cluster_modules.all <- c("cluster_0.module", "cluster_1.module", "cluster_2.module", "cluster_3.module", "cluster_4.module", "cluster_5.module", "cluster_6.module", "cluster_7.module", "cluster_8.module", "cluster_9.module")

for (gene in astrocyte_markers) {
  GSM5026144_S7_Seurat <-
    AddModuleScore(object = GSM5026144_S7_Seurat,
                   features = list(gene),
                   name = gene)
}

for (cluster_module_name in cluster_modules.all) {
  # Retrieve the actual module list using get()
  cluster_module <- get(cluster_module_name)

  # Add the module score to the Seurat object
  GSM5026144_S7_Seurat <- AddModuleScore(object = GSM5026144_S7_Seurat, features = cluster_module, name = cluster_module_name)
}

Warning: The following features are not present in the object: Gm4951, not searching for symbol synonyms

# GSM5026144_S7_Seurat <- AddModuleScore(object = GSM5026144_S7_Seurat, features = cluster_0.module, name = "Cluster0_ModuleScore")
# GSM5026144_S7_Seurat <- AddModuleScore(object = GSM5026144_S7_Seurat, features = cluster_1.module, name = "Cluster1_ModuleScore")
# GSM5026144_S7_Seurat <- AddModuleScore(object = GSM5026144_S7_Seurat, features = cluster_2.module, name = "Cluster2_ModuleScore")
# GSM5026144_S7_Seurat <- AddModuleScore(object = GSM5026144_S7_Seurat, features = cluster_3.module, name = "Cluster3_ModuleScore")
# GSM5026144_S7_Seurat <- AddModuleScore(object = GSM5026144_S7_Seurat, features = cluster_4.module, name = "Cluster4_ModuleScore")
# GSM5026144_S7_Seurat <- AddModuleScore(object = GSM5026144_S7_Seurat, features = cluster_5.module, name = "Cluster5_ModuleScore")
# GSM5026144_S7_Seurat <- AddModuleScore(object = GSM5026144_S7_Seurat, features = cluster_6.module, name = "Cluster6_ModuleScore")
# GSM5026144_S7_Seurat <- AddModuleScore(object = GSM5026144_S7_Seurat, features = cluster_7.module, name = "Cluster7_ModuleScore")
# GSM5026144_S7_Seurat <- AddModuleScore(object = GSM5026144_S7_Seurat, features = cluster_8.module, name = "Cluster8_ModuleScore")
# GSM5026144_S7_Seurat <- AddModuleScore(object = GSM5026144_S7_Seurat, features = cluster_9.module, name = "Cluster9_ModuleScore")

# List to store individual plots
plots <- list()

features_list <- list(
  "Aldh1l11" = "Aldh1l1",
  "Slc1a21" = "Slc1a2",
  "S100b1" = "S100b",
  "Gfap1" = "Gfap",
  "Myoc1" = "Myoc",
  "cluster_0.module1" = "0",
  "cluster_1.module1" = "1",
  "cluster_2.module1" = "2",
  "cluster_3.module1" = "3",
  "cluster_4.module1" = "4",
  "cluster_5.module1" = "5",
  "cluster_6.module1" = "6",
  "cluster_7.module1" = "7",
  "cluster_8.module1" = "8",
  "cluster_9.module1" = "9"
)

legend_only <-
  SpatialPlot(
    GSM5026144_S7_Seurat,
    features = "cluster_9.module1",
    pt.size.factor = 0,
    image.alpha = 0
  ) + theme(legend.text = element_blank(), legend.title = element_blank())

# Add the legend_only plot to the the plots list
plots[[length(plots) + 1]] <- legend_only

# Create an image-only ggplot
image_only <-
  SpatialPlot(GSM5026144_S7_Seurat,
              features = "cluster_9.module1",
              pt.size.factor = 0) + theme(legend.position = "none") +
  annotate(
    "text",
    x = Inf,
    y = Inf,
    label = "H&E",
    hjust = 1.1,
    vjust = 1.1,
    fontface = "italic",
    color = "black",
    size = 2
  )

  
# Add the image-only plot to the the plots list
plots[[length(plots) + 1]] <- image_only

# Generate the plots
for (feature in names(features_list)) {
  # Create a spatial feature plot for each feature
  p <- SpatialFeaturePlot(GSM5026144_S7_Seurat, features = feature) + 
       theme(legend.position = "none") +
       annotate("text", x = Inf, y = Inf, label = features_list[[feature]], 
                hjust = 1.1, vjust = 1.1, fontface = "italic", color = "black", size = 2) 

  # Store the plot in the list
  plots[[feature]] <- p
}

# Arrange plots in a grid
grid.arrange(grobs = plots, ncol = 5)

# Load the data into a Seurat object
ours_GSM5026144_S7_Seurat <- Load10X_Spatial(
  data.dir = dir.Spatial_H5,
  filename = "GSM5026144_S7_filtered_feature_bc_matrix.h5",
  assay = "Spatial",
  slice = "slice1",
  filter.matrix = TRUE,
  to.upper = FALSE,
  image = NULL
)

ours_GSM5026144_S7_Seurat <-
  SCTransform(
    ours_GSM5026144_S7_Seurat,
    assay = 'Spatial',
    return.only.var.genes = F,
    verbose = FALSE
  )

# Subset data for avg_logFC > 0.35
astro.cluster_markers.subset.0.35 <- astro.markers[astro.markers$avg_log2FC > 0.35, ]

# Remove ensemble ID from gene names
astro.cluster_markers.subset.0.35$gene <- sub(".*\\.", "", astro.cluster_markers.subset.0.35$gene)

# Specify the list of clusters
specified_clusters <- c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)

# Loop through each specified cluster
for (cluster in specified_clusters) {
  
  # Subset data for the current cluster
  cluster_data <- astro.cluster_markers.subset.0.35[astro.cluster_markers.subset.0.35$cluster == cluster, ]
  
  # Create a comma-separated gene list with each gene in quotes
  gene_list <- sprintf('"%s"', cluster_data$gene)
  gene_list_string <- paste(gene_list, collapse = ", ")
  
  # Create a list with the gene names and assign it to a named variable
  variable_name <- paste0("cluster_", cluster, "_ours_module")
  assign(variable_name, list(eval(parse(text = paste("c(", gene_list_string, ")")))), envir = .GlobalEnv)
}

# Initialize an empty list to store the cluster variables
cluster_vars <- list()

# Loop through each specified cluster
for (cluster in specified_clusters) {
  
  # Subset data for the current cluster
  cluster_data <- astro.cluster_markers.subset.0.35[astro.cluster_markers.subset.0.35$cluster == cluster, ]
  
  # Create a comma-separated gene list with each gene in quotes
  gene_list <- sprintf('"%s"', cluster_data$gene)
  gene_list_string <- paste(gene_list, collapse = ", ")
  
  # Create a list with the gene names and assign it to a named variable
  variable_name <- paste0("cluster_", cluster, "_ours_module")
  variable_value <- list(eval(parse(text = paste("c(", gene_list_string, ")"))))
  assign(variable_name, variable_value, envir = .GlobalEnv)
  
  # Add the variable name to the list of cluster variables
  cluster_vars <- append(cluster_vars, variable_name)
}

cluster_vars 

[[1]]
[1] "cluster_0_ours_module"

[[2]]
[1] "cluster_1_ours_module"

[[3]]
[1] "cluster_2_ours_module"

[[4]]
[1] "cluster_3_ours_module"

[[5]]
[1] "cluster_4_ours_module"

[[6]]
[1] "cluster_5_ours_module"

[[7]]
[1] "cluster_6_ours_module"

[[8]]
[1] "cluster_7_ours_module"

[[9]]
[1] "cluster_8_ours_module"

[[10]]
[1] "cluster_9_ours_module"

for (gene in astrocyte_markers) {
  ours_GSM5026144_S7_Seurat <-
    AddModuleScore(object = ours_GSM5026144_S7_Seurat, features = list(gene), name = gene)
}

# Loop through each cluster variable
for (cluster_var in cluster_vars) {
  
  # Extract the cluster number from the variable name
  cluster_number <- gsub("\\D", "", cluster_var)
  
  # Create the name for the module score
  module_score_name <- paste("cluster_", cluster_number, ".ours_module", sep = "")
  
  # Add the module score
  ours_GSM5026144_S7_Seurat <- AddModuleScore(
    object = ours_GSM5026144_S7_Seurat,
    features = get(cluster_var),
    name = module_score_name
  )
}

Warning: The following features are not present in the object: Fam212b, not searching for symbol synonymsWarning: The following features are not present in the object: 1110008F13Rik, Usmg5, 2410015M20Rik, Pyurf, 2010107E04Rik, 1500011K16Rik, Minos1, Fam213a, Tmem5, Aes, Pla2g16, D10Jhu81e, 1110004E09Rik, Fam96a, Lao1, 5930412G12Rik, 1, not searching for symbol synonymsWarning: The following features are not present in the object: Gm26772, Gm26870, Cd27, Gm10600, C130073E24Rik, Gm11713, Gm26909, 1810041L15Rik, B430010I23Rik, A330069E16Rik, not searching for symbol synonymsWarning: The following features are not present in the object: Fam213a, Pyurf, Pla2g16, 1, 1110004E09Rik, 5930412G12Rik, not searching for symbol synonymsWarning: The following features are not present in the object: Cxcl9, Mx1, Gm4951, 2410015M20Rik, Tmem5, Fam213a, F830016B08Rik, Pla2g16, 1500015O10Rik, 1, Fam19a5, Gm8186, 2900011O08Rik, Slc10a6, not searching for symbol synonymsWarning: The following features are not present in the object: Slc10a6, 1110004E09Rik, Cxcl9, Gm10600, 1, 1500015O10Rik, 2900011O08Rik, Cyr61, Gm8186, Fam19a5, not searching for symbol synonymsWarning: The following features are not present in the object: 1500015O10Rik, C130073E24Rik, Mx1, Gm26772, Gm26909, Cxcl9, Fam19a5, 1, Fam212b, 1500011K16Rik, 2900011O08Rik, 2410015M20Rik, Gm8186, Aes, Pyurf, Minos1, Usmg5, Pla2g16, Gm26870, D10Jhu81e, 2010107E04Rik, 1110004E09Rik, not searching for symbol synonymsWarning: The following features are not present in the object: Fam19a1, 1810041L15Rik, Cd27, not searching for symbol synonymsWarning: The following features are not present in the object: Gm4951, F830016B08Rik, Slc10a6, Cxcl9, Mx1, Slco1b2, not searching for symbol synonymsWarning: The following features are not present in the object: 2010107E04Rik, Fam213a, Usmg5, 2410015M20Rik, Minos1, Cxcl9, 1500011K16Rik, 1500015O10Rik, Aes, Gm8186, 2900011O08Rik, 1110008F13Rik, Lao1, Pla2g16, not searching for symbol synonyms

# List to store individual plots
our_plots <- list()

features_list <- list(
  "Aldh1l11" = "Aldh1l1",
  "Slc1a21" = "Slc1a2",
  "S100b1" = "S100b",
  "Gfap1" = "Gfap",
  "Myoc1" = "Myoc",
  "cluster_0.ours_module1" = "0",
  "cluster_1.ours_module1" = "1",
  "cluster_2.ours_module1" = "2",
  "cluster_3.ours_module1" = "3",
  "cluster_4.ours_module1" = "4",
  "cluster_5.ours_module1" = "5",
  "cluster_6.ours_module1" = "6",
  "cluster_7.ours_module1" = "7",
  "cluster_8.ours_module1" = "8",
  "cluster_9.ours_module1" = "9"
)

ours_legend_only <- SpatialPlot(ours_GSM5026144_S7_Seurat, features = "cluster_9.ours_module1", pt.size.factor = 0, image.alpha = 0) + theme(legend.text = element_blank(), legend.title = element_blank())

# Add the legend_only plot to the the plots list
our_plots[[length(our_plots) + 1]] <- ours_legend_only

# Create an image-only ggplot
ours_image_only <- SpatialPlot(ours_GSM5026144_S7_Seurat, features = "cluster_9.ours_module1", pt.size.factor = 0) + theme(legend.position = "none")
  
# Add the image-only plot to the the plots list
our_plots[[length(our_plots) + 1]] <- ours_image_only

# Generate the plots
for (feature in names(features_list)) {
  # Create a spatial feature plot for each feature
  p <- SpatialFeaturePlot(ours_GSM5026144_S7_Seurat, features = feature) + 
       theme(legend.position = "none") +
       annotate("text", x = Inf, y = Inf, label = features_list[[feature]], 
                hjust = 1.1, vjust = 1.1, fontface = "italic", color = "black", size = 2)  
  # Store the plot in the list
  our_plots[[feature]] <- p
}

# Arrange plots in a grid
grid.arrange(grobs = our_plots, ncol = 5)

sessionInfo()

---
title: "Astrocytes - cluster analysis"
output:
  html_document:
    df_print: paged
    toc: yes
  html_notebook:
    code_folding: show
    highlight: kate
    toc: yes
    toc_float:
      toc_collapsed: no
---

```{r setup, include = FALSE}
library(cowplot)
library(tidyverse)
library(grid)
library(gridExtra)
library(muscat)
library(purrr)
library(SingleCellExperiment)
library(UpSetR)
library(Seurat)
library(ComplexHeatmap)
library(circlize)
library(viridis)
library(ggridges)
library(dplyr)
library(imager)

# absolute path to the working directory and the current objects directory
wd <- "/home/bench-user/data"
dir.objects <- file.path(wd, "objects", "nv.v1_2023-11-05/")
```

# Load data

```{r}
samples.df <- readRDS(file.path(dir.objects, "metadata.rds"))
samples.df
```


```{r load-data}
so.astro <- readRDS(file.path(dir.objects, "so.astro.rds"))

#so.astro@meta.data$ident <- as.factor(so.astro@meta.data$integrated_snn_res.0.2)

Idents(so.astro) <- "integrated_snn_res.0.2"

# Prepare a vector of colors for the clusters
clusters <- levels(Idents(so.astro)) %>% as.numeric()
cluster.cols <- muscat:::.cluster_colors[seq_along(clusters)]
names(cluster.cols) <- clusters

clusters
```

```{r}
astro.markers <- readRDS(file.path(dir.objects, "astro.markers.v1.rds")) %>% 
  remove_rownames() %>% 
  mutate(gene.name = word(gene, 2, sep = "\\."))
head(astro.markers)
```

# Fig. 2b. Astrocytes saline vs LPS by sex

```{r, fig.width=12, fig.height=4}
DimPlot(
  so.astro,
  reduction = "tsne",
  group.by = "integrated_snn_res.0.2",
  split.by = "group_id"
) +
  scale_color_manual(values = cluster.cols)
```

# Fig. 2c. Genes statistically enriched in each cluster

### Subset of genes used for plotting

```{r fig.height=12, fig.width=5, message=FALSE}
astro.markers.subset <- astro.markers %>% 
  group_by(cluster) %>%
  slice_max(n = 15, order_by = avg_log2FC)
astro.markers.subset
```




```{r fig.height=14, fig.width=8, message=FALSE, warning=FALSE}
# Expression matrix
cells_to_clusters <- data.frame(cluster = so.astro$integrated_snn_res.0.2) %>% 
  rownames_to_column("cell")

geneset <- unique(astro.markers.subset$gene)

mat.scale.ours_data <-
  so.astro@assays$integrated@scale.data[geneset, cells_to_clusters$cell]

cluster_indices <-
  split(cells_to_clusters$cell, cells_to_clusters$cluster)

cluster_means <- sapply(cluster_indices, function(indices) {
  Matrix::rowMeans(mat.scale.ours_data[, indices, drop = FALSE])
})

rownames(cluster_means) <-
  word(rownames(cluster_means), 2, sep = "\\.")

# Gene cluster color bar annotation
# by cluster order
genes.clusters.cols <- astro.markers.subset %>%
  select(gene, cluster) %>%
  mutate(gene.full = gene,
         gene = word(gene.full, 2, sep = "\\.")) %>%
  group_by(gene) %>%
  slice_min(n = 1, order_by = cluster) %>%
  ungroup() %>%
  arrange(cluster) %>%
  left_join(., enframe(cluster.cols, name = "cluster", value = "colors"))
rownames(genes.clusters.cols) <- genes.clusters.cols$gene
genes.clusters.cols <-
  genes.clusters.cols[rownames(cluster_means),]

row.anno.genes <-
  rowAnnotation(
    "Cluster" = genes.clusters.cols$cluster,
    col = list("Cluster" = cluster.cols),
    show_annotation_name = FALSE
  )

# Define the range and breaks
breaks <-
  seq(0, 1, length.out = 11)  # This creates 11 points between 0 and 1

colors <- plasma(11)

# Create a color function that maps the range [0, 1] to the viridis palette
color_mapping <- colorRamp2(breaks, colors)

# Use the adjusted col argument
Heatmap(
  cluster_means,
  name = "Normal. expression",
  col = color_mapping,
  cluster_rows = FALSE,
  show_row_dend = FALSE,
  cluster_columns = FALSE,
  column_names_side = "bottom",
  row_names_side = "left",
  column_names_rot = 0,
  column_names_centered = TRUE,
  row_names_gp = gpar(fontsize = 8),
  left_annotation = row.anno.genes
)
```

# Spatial plots

```{r}
# Specify the directory containing the H5 file and the image data
dir.Spatial_H5 <- file.path(wd, "GSM5026144")

# Load the data into a Seurat object
GSM5026144_S7_Seurat <- Load10X_Spatial(
  data.dir = dir.Spatial_H5,
  filename = "GSM5026144_S7_filtered_feature_bc_matrix.h5",
  assay = "Spatial",
  slice = "slice1",
  filter.matrix = TRUE,
  to.upper = FALSE,
  image = NULL
)
```


```{r}
GSM5026144_S7_Seurat <-
  SCTransform(
    object = GSM5026144_S7_Seurat,
    assay = 'Spatial',
    return.only.var.genes = F,
    verbose = FALSE
  )
GSM5026144_S7_Seurat
```


```{r}
astrocyte_markers <- c("Aldh1l1", "Slc1a2", "S100b", "Gfap", "Myoc")

cluster_0.module <- list(c("Gm42418", "Arhgap31", "Kcnq1ot1", "Rmst", "Cit", "Gli1", "Ttll3", "Neat1", "Gm26917"))
cluster_1.module <- list(c("Mfge8", "Igfbp2", "Btbd17"))
cluster_2.module <- list(c("Dbi", "Agt", "Nnat", "Ppia", "Tmsb4x", "Scrg1", "Mt3", "Gstm1", "Nkain4", "S100a1"))
cluster_3.module <- list(c("Thrsp", "Actb", "Chchd10", "Hint1", "Mrpl41", "Mpc1", "Camk2n1", "Ndufc2", "Nupr1", "Igfbp2"))
cluster_4.module <- list(c("Lcn2", "Ifitm3", "Timp1", "B2m", "Vim", "Gfap", "Psmb8", "Bst2"))
cluster_5.module <- list(c("Crym", "Prss56", "Slc43a3", "Mgst1", "Slc1a5", "Tuba1a", "Itm2b", "Chil1", "Ptn"))
cluster_6.module <- list(c("Thbs4", "Igfbp5", "Fxyd6", "Gsn", "Fxyd1", "Cd9", "Meg3"))
cluster_7.module <- list(c("Itih3", "Sparc", "Nkx6-2", "Etnppl", "Gria1", "Spon1", "5031439G07Rik"))
cluster_8.module <- list(c("Igtp", "Gm4951", "Ifit3", "Iigp1", "Irgm1", "Tap1", "Gbp2", "Ifit1", "Isg15", "Cxcl10"))
cluster_9.module <- list(c("Zic1", "Slc38a1", "Col1a2", "Kcnk2", "Luzp2", "Mgll", "Zic4", "Hopx"))

cluster_modules.all <- c("cluster_0.module", "cluster_1.module", "cluster_2.module", "cluster_3.module", "cluster_4.module", "cluster_5.module", "cluster_6.module", "cluster_7.module", "cluster_8.module", "cluster_9.module")

for (gene in astrocyte_markers) {
  GSM5026144_S7_Seurat <-
    AddModuleScore(object = GSM5026144_S7_Seurat,
                   features = list(gene),
                   name = gene)
}

for (cluster_module_name in cluster_modules.all) {
  # Retrieve the actual module list using get()
  cluster_module <- get(cluster_module_name)

  # Add the module score to the Seurat object
  GSM5026144_S7_Seurat <- AddModuleScore(object = GSM5026144_S7_Seurat, features = cluster_module, name = cluster_module_name)
}
```

```{r}
# List to store individual plots
plots <- list()

features_list <- list(
  "Aldh1l11" = "Aldh1l1",
  "Slc1a21" = "Slc1a2",
  "S100b1" = "S100b",
  "Gfap1" = "Gfap",
  "Myoc1" = "Myoc",
  "cluster_0.module1" = "0",
  "cluster_1.module1" = "1",
  "cluster_2.module1" = "2",
  "cluster_3.module1" = "3",
  "cluster_4.module1" = "4",
  "cluster_5.module1" = "5",
  "cluster_6.module1" = "6",
  "cluster_7.module1" = "7",
  "cluster_8.module1" = "8",
  "cluster_9.module1" = "9"
)

legend_only <-
  SpatialPlot(
    GSM5026144_S7_Seurat,
    features = "cluster_9.module1",
    pt.size.factor = 0,
    image.alpha = 0
  ) + theme(legend.text = element_blank(), legend.title = element_blank())

# Add the legend_only plot to the the plots list
plots[[length(plots) + 1]] <- legend_only

# Create an image-only ggplot
image_only <-
  SpatialPlot(GSM5026144_S7_Seurat,
              features = "cluster_9.module1",
              pt.size.factor = 0) + theme(legend.position = "none") +
  annotate(
    "text",
    x = Inf,
    y = Inf,
    label = "H&E",
    hjust = 1.1,
    vjust = 1.1,
    fontface = "italic",
    color = "black",
    size = 2
  )
  
# Add the image-only plot to the the plots list
plots[[length(plots) + 1]] <- image_only

# Generate the plots
for (feature in names(features_list)) {
  # Create a spatial feature plot for each feature
  p <- SpatialFeaturePlot(GSM5026144_S7_Seurat, features = feature) + 
       theme(legend.position = "none") +
       annotate("text", x = Inf, y = Inf, label = features_list[[feature]], 
                hjust = 1.1, vjust = 1.1, fontface = "italic", color = "black", size = 2) 

  # Store the plot in the list
  plots[[feature]] <- p
}
```

```{r, warning=FALSE, fig.width=10, fig.height=10}
# Arrange plots in a grid
grid.arrange(grobs = plots, ncol = 5)
```

With our analysis markers at 0.35 LogFC Cutoff

```{r}
# Load the data into a Seurat object
ours_GSM5026144_S7_Seurat <- Load10X_Spatial(
  data.dir = dir.Spatial_H5,
  filename = "GSM5026144_S7_filtered_feature_bc_matrix.h5",
  assay = "Spatial",
  slice = "slice1",
  filter.matrix = TRUE,
  to.upper = FALSE,
  image = NULL
)

ours_GSM5026144_S7_Seurat <-
  SCTransform(
    ours_GSM5026144_S7_Seurat,
    assay = 'Spatial',
    return.only.var.genes = F,
    verbose = FALSE
  )
```


```{r}
# Subset data for avg_logFC > 0.35
astro.cluster_markers.subset.0.35 <- astro.markers[astro.markers$avg_log2FC > 0.35, ]

# Remove ensemble ID from gene names
astro.cluster_markers.subset.0.35$gene <- sub(".*\\.", "", astro.cluster_markers.subset.0.35$gene)

# Specify the list of clusters
specified_clusters <- c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)

# Loop through each specified cluster
for (cluster in specified_clusters) {
  
  # Subset data for the current cluster
  cluster_data <- astro.cluster_markers.subset.0.35[astro.cluster_markers.subset.0.35$cluster == cluster, ]
  
  # Create a comma-separated gene list with each gene in quotes
  gene_list <- sprintf('"%s"', cluster_data$gene)
  gene_list_string <- paste(gene_list, collapse = ", ")
  
  # Create a list with the gene names and assign it to a named variable
  variable_name <- paste0("cluster_", cluster, "_ours_module")
  assign(variable_name, list(eval(parse(text = paste("c(", gene_list_string, ")")))), envir = .GlobalEnv)
}

# Initialize an empty list to store the cluster variables
cluster_vars <- list()

# Loop through each specified cluster
for (cluster in specified_clusters) {
  
  # Subset data for the current cluster
  cluster_data <- astro.cluster_markers.subset.0.35[astro.cluster_markers.subset.0.35$cluster == cluster, ]
  
  # Create a comma-separated gene list with each gene in quotes
  gene_list <- sprintf('"%s"', cluster_data$gene)
  gene_list_string <- paste(gene_list, collapse = ", ")
  
  # Create a list with the gene names and assign it to a named variable
  variable_name <- paste0("cluster_", cluster, "_ours_module")
  variable_value <- list(eval(parse(text = paste("c(", gene_list_string, ")"))))
  assign(variable_name, variable_value, envir = .GlobalEnv)
  
  # Add the variable name to the list of cluster variables
  cluster_vars <- append(cluster_vars, variable_name)
}

cluster_vars 

for (gene in astrocyte_markers) {
  ours_GSM5026144_S7_Seurat <-
    AddModuleScore(object = ours_GSM5026144_S7_Seurat, features = list(gene), name = gene)
}

# Loop through each cluster variable
for (cluster_var in cluster_vars) {
  
  # Extract the cluster number from the variable name
  cluster_number <- gsub("\\D", "", cluster_var)
  
  # Create the name for the module score
  module_score_name <- paste("cluster_", cluster_number, ".ours_module", sep = "")
  
  # Add the module score
  ours_GSM5026144_S7_Seurat <- AddModuleScore(
    object = ours_GSM5026144_S7_Seurat,
    features = get(cluster_var),
    name = module_score_name
  )
}
```




```{r}
# List to store individual plots
our_plots <- list()

features_list <- list(
  "Aldh1l11" = "Aldh1l1",
  "Slc1a21" = "Slc1a2",
  "S100b1" = "S100b",
  "Gfap1" = "Gfap",
  "Myoc1" = "Myoc",
  "cluster_0.ours_module1" = "0",
  "cluster_1.ours_module1" = "1",
  "cluster_2.ours_module1" = "2",
  "cluster_3.ours_module1" = "3",
  "cluster_4.ours_module1" = "4",
  "cluster_5.ours_module1" = "5",
  "cluster_6.ours_module1" = "6",
  "cluster_7.ours_module1" = "7",
  "cluster_8.ours_module1" = "8",
  "cluster_9.ours_module1" = "9"
)

ours_legend_only <- SpatialPlot(ours_GSM5026144_S7_Seurat, features = "cluster_9.ours_module1", pt.size.factor = 0, image.alpha = 0) + theme(legend.text = element_blank(), legend.title = element_blank())

# Add the legend_only plot to the the plots list
our_plots[[length(our_plots) + 1]] <- ours_legend_only

# Create an image-only ggplot
ours_image_only <- SpatialPlot(ours_GSM5026144_S7_Seurat, features = "cluster_9.ours_module1", pt.size.factor = 0) + theme(legend.position = "none")
  
# Add the image-only plot to the the plots list
our_plots[[length(our_plots) + 1]] <- ours_image_only

# Generate the plots
for (feature in names(features_list)) {
  # Create a spatial feature plot for each feature
  p <- SpatialFeaturePlot(ours_GSM5026144_S7_Seurat, features = feature) + 
       theme(legend.position = "none") +
       annotate("text", x = Inf, y = Inf, label = features_list[[feature]], 
                hjust = 1.1, vjust = 1.1, fontface = "italic", color = "black", size = 2)  
  # Store the plot in the list
  our_plots[[feature]] <- p
}
```

```{r, warning=FALSE, fig.width=10, fig.height=10}
# Arrange plots in a grid
grid.arrange(grobs = our_plots, ncol = 5)
```



```{r}
sessionInfo()
```





