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03_scRNAseq_CulsterAssignment.R
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03_scRNAseq_CulsterAssignment.R
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library(Seurat)
library(ggplot2)
library(stringr)
library(writexl)
library(gridExtra)
library(tidyverse)
print(getwd())
dir.create("./Results")
list.dirs()
print(getwd())
dataPath <- "G:\\Shared drives\\Gonzalez-Vicente Lab\\03_Data\\2023_Rat scRNAseq\\01_Resolution 1.6\\Results\\ALL.combined.rds"
print(dataPath)
all.combined <- readRDS(dataPath)
DimPlot(object = all.combined,
reduction = "umap",
label = T)
ggsave(filename = paste("./Results/01_ALL clusters_UMAP", ".tiff", sep = ""),
device = "tiff",
dpi = 600)
DimPlot(object = all.combined,
reduction = "tsne",
label = T)
ggsave(filename = paste("./Results/01_ALL clusters_TSNE", ".tiff", sep = ""),
device = "tiff",
dpi = 600)
DimPlot(object = all.combined,
reduction = "pca",
label = T)
ggsave(filename = paste("./Results/01_ALL clusters_PCA", ".tiff", sep = ""),
device = "tiff",
dpi = 600)
all.combined$Cell.Class <- case_when(
all.combined$seurat_clusters %in% c(26,29,19) ~ "Endothelial",
all.combined$seurat_clusters %in% c(1,18,3,0,4,13,27,23,30,12,11,9,22,7,35,25,21,32,15,14,16,6,31,17) ~ "Epithelial",
all.combined$seurat_clusters %in% c(24,34,10,28,5,2,8,20) ~ "Immune",
all.combined$seurat_clusters %in% c(33) ~ "Stromal")
table(all.combined$Cell.Class)
DimPlot(object = all.combined,
reduction = "umap",
group.by = "Cell.Class",
label = T)
ggsave(filename = paste("./Results/02_Cell.Class_UMAP", ".tiff", sep = ""),
device = "tiff",
dpi = 600)
DimPlot(object = all.combined,
reduction = "tsne",
group.by = "Cell.Class",
label = T)
ggsave(filename = paste("./Results/02_Cell.Class_TSNE", ".tiff", sep = ""),
device = "tiff",
dpi = 600)
DimPlot(object = all.combined,
reduction = "pca",
group.by = "Cell.Class",
repel = T,
label = T)
ggsave(filename = paste("./Results/02_Cell.Class_PCA", ".tiff", sep = ""),
device = "tiff",
dpi = 600)
all.combined <- SetIdent(all.combined, value = all.combined$Cell.Class)
Epithelial <- subset(all.combined,
idents = "Epithelial")
DimPlot(object = Epithelial,
reduction = "umap",
group.by = "Cell.Class",
repel = T,
label = T)
ggsave(filename = paste("./Results/03_Cell.Class_UMAP", ".tiff", sep = ""),
device = "tiff",
dpi = 600)
DimPlot(object = Epithelial,
reduction = "tsne",
group.by = "Cell.Class",
repel = T,
label = T)
ggsave(filename = paste("./Results/03_Cell.Class_TSNE", ".tiff", sep = ""),
device = "tiff",
dpi = 600)
DimPlot(object = Epithelial,
reduction = "pca",
group.by = "Cell.Class",
repel = T,
label = T)
ggsave(filename = paste("./Results/03_Cell.Class_PCA", ".tiff", sep = ""),
device = "tiff",
dpi = 600)
Epithelial <- SetIdent(Epithelial, value = Epithelial$seurat_clusters)
HubMAP_Curated <- c(
"Slc5a12", # PT_S1
"Slc22a8","Slc22a6", # PT_S1/S2
"Slc22a7", "Slc7a13", # PT_S3
"Satb2", # PT_S3/DTL
"Spp1", # DTL
"Cldn3", # ATL
"Prox1", # M_TAL
"Slc12a1", # TAL
"Cldn16","Enox1", # C_TAL
"Slc12a3", # DCT
"Scnn1g", # CNT/CD_PC
"Slc4a1", # CNT/CD_IC.A
"Slc26a4", # CD_IC.B
"Slc14a1" # Papillary Tip
#"Slc14a2" # IMCD/DTL
)
DotPlot(Epithelial,
features = HubMAP_Curated,
assay = "RNA",
cols = c("white", "blue"),
scale = T,
scale.by = "radius",
dot.scale = 6) +
RotatedAxis()
ggsave(filename = paste("./Results/04_Cluster Markers_Epithelial", ".tiff", sep = ""),
bg = "white",
device = "tiff",
dpi = 600)
table(all.combined$seurat_clusters)
all.combined$Cell.Type1 <- case_when(
all.combined$seurat_clusters %in% c(26,29,19,24,34,10,28,5,2,8,20,33) ~ NA,
all.combined$seurat_clusters %in% c(1,18) ~ "PT.S1",
all.combined$seurat_clusters %in% c(0,3) ~ "PT.S2",
all.combined$seurat_clusters %in% c(4,13) ~ "PT.S3",
all.combined$seurat_clusters %in% c(12) ~ "DTL",
all.combined$seurat_clusters %in% c(30) ~ "DTL",
all.combined$seurat_clusters %in% c(23) ~ "ATL",
all.combined$seurat_clusters %in% c(22,9) ~ "MTAL",
all.combined$seurat_clusters %in% c(7,35) ~ "CTAL",
all.combined$seurat_clusters %in% c(25,11) ~ "CTAL",
all.combined$seurat_clusters %in% c(21) ~ "DCT",
all.combined$seurat_clusters %in% c(16,14) ~ "IC.A",
all.combined$seurat_clusters %in% c(6,31) ~ "IC.B",
all.combined$seurat_clusters %in% c(15) ~ "PC",
all.combined$seurat_clusters %in% c(32) ~ "IMCD",
all.combined$seurat_clusters %in% c(17,27) ~ NA)
table(all.combined$Cell.Type1)
all.combined$Cell.Type2 <- case_when(
all.combined$seurat_clusters %in% c(26,29,19,24,34,10,28,5,2,8,20,33) ~ NA,
all.combined$seurat_clusters %in% c(1,18) ~ "PT",
all.combined$seurat_clusters %in% c(0,3) ~ "PT",
all.combined$seurat_clusters %in% c(4,13) ~ "PT",
all.combined$seurat_clusters %in% c(12) ~ "DTL",
all.combined$seurat_clusters %in% c(30) ~ "DTL",
all.combined$seurat_clusters %in% c(23) ~ "ATL",
all.combined$seurat_clusters %in% c(22,9) ~ "TAL",
all.combined$seurat_clusters %in% c(7,35) ~ "TAL",
all.combined$seurat_clusters %in% c(25,11) ~ "TAL",
all.combined$seurat_clusters %in% c(21) ~ "DCT",
all.combined$seurat_clusters %in% c(16,14) ~ "IC",
all.combined$seurat_clusters %in% c(6,31) ~ "IC",
all.combined$seurat_clusters %in% c(15) ~ "PC",
all.combined$seurat_clusters %in% c(32) ~ "IMCD",
all.combined$seurat_clusters %in% c(17,27) ~ NA)
table(all.combined$Cell.Type2)
all.combined$Region <- case_when(
all.combined$seurat_clusters %in% c(26,29,19,24,34,10,28,5,2,8,20,33) ~ NA,
all.combined$seurat_clusters %in% c(1,18) ~ "PT",
all.combined$seurat_clusters %in% c(0,3) ~ "PT",
all.combined$seurat_clusters %in% c(4,13) ~ "PT",
all.combined$seurat_clusters %in% c(12) ~ "Thin.limbs",
all.combined$seurat_clusters %in% c(30) ~ "Thin.limbs",
all.combined$seurat_clusters %in% c(23) ~ "Thin.limbs",
all.combined$seurat_clusters %in% c(22,9) ~ "TAL",
all.combined$seurat_clusters %in% c(7,35) ~ "TAL",
all.combined$seurat_clusters %in% c(25,11) ~ "TAL",
all.combined$seurat_clusters %in% c(21) ~ "DCT",
all.combined$seurat_clusters %in% c(16,14) ~ "CD",
all.combined$seurat_clusters %in% c(6,31) ~ "CD",
all.combined$seurat_clusters %in% c(15) ~ "CD",
all.combined$seurat_clusters %in% c(32) ~ "CD",
all.combined$seurat_clusters %in% c(17,27) ~ NA)
table(all.combined$Region)
rm(Epithelial)
all.combined <- SetIdent(all.combined, value = all.combined$Cell.Class)
Epithelial <- subset(all.combined,
idents = "Epithelial")
Epithelial <- SetIdent(Epithelial, value = Epithelial$Cell.Type1)
Epithelial <- subset(Epithelial,
idents = c("PT.S1",
"PT.S2",
"PT.S3",
"DTL",
"ATL",
"MTAL",
"CTAL",
"DCT",
"PC",
"IC.A",
"IC.B",
"IMCD"))
Epithelial@active.ident <- factor(Epithelial@active.ident,
levels=c("PT.S1",
"PT.S2",
"PT.S3",
"DTL",
"ATL",
"MTAL",
"CTAL",
"DCT",
"PC",
"IC.A",
"IC.B",
"IMCD"
))
DimPlot(object = Epithelial,
reduction = "umap",
repel = T,
label = T)
ggsave(filename = paste("./Results/05_Cell.Type1_UMAP", ".tiff", sep = ""),
device = "tiff",
dpi = 600)
DimPlot(object = Epithelial,
reduction = "tsne",
repel = T,
label = T)
ggsave(filename = paste("./Results/05_Cell.Type1_TSNE", ".tiff", sep = ""),
device = "tiff",
dpi = 600)
DimPlot(object = Epithelial,
reduction = "pca",
repel = T,
label = T)
ggsave(filename = paste("./Results/05_Cell.Type1_PCA", ".tiff", sep = ""),
device = "tiff",
dpi = 600)
table(Epithelial$Cell.Type1)
DotPlot(Epithelial,
features = HubMAP_Curated,
assay = "RNA",
cols = c("white", "blue"),
scale = T,
scale.by = "radius",
dot.scale = 6) +
RotatedAxis()
ggsave(filename = paste("./Results/06_DotPlot_Cell.Type1", ".tiff", sep = ""),
bg = "white",
device = "tiff",
dpi = 600)
dir.create(file.path("./Results", "Cluster Markers_Cell.Type1"), showWarnings = T)
DefaultAssay(object = Epithelial) <- "RNA"
markers_list <- list()
# Loop over each cell identity (cluster)
for (clus in unique(Epithelial@active.ident)) {
# Find markers for the current cluster
markers <- FindMarkers(Epithelial,
ident.1 = clus,
logfc.threshold = 1,
min.pct = 0.25,
test.use = "wilcox",
only.pos = FALSE,
max.cells.per.ident = Inf,
random.seed = 1,
verbose = TRUE)
# Add the markers to the list, with the cluster identity as the list name
markers_list[[paste("Cluster", clus, sep = " ")]] <- markers
# Run garbage collection and remove objects from memory
gc(verbose = TRUE)
}
# Write the markers to separate CSV files
lapply(names(markers_list), function(x) {
write.csv(markers_list[[x]],
file = paste0("./Results/Cluster Markers_Cell.Type1/", x, "_Markers.csv"),
row.names = T)
})
dir.create(file.path("./Results", "Cluster Averages_Cell.Type1"), showWarnings = T)
cluster.averages <- AverageExpression(Epithelial)
head(cluster.averages[["RNA"]][1:10,])
write.csv(cluster.averages$RNA, "./Results/Cluster Averages_Cell.Type1/Cell.Type1 Clusters mean RNA expression.csv")
cluster.averages <- AverageExpression(Epithelial)
head(cluster.averages[["SCT"]][1:10,])
write.csv(cluster.averages$SCT, "./Results/Cluster Averages_Cell.Type1/Cell.Type1 Clusters mean SCT expression.csv")
# Write capitalized genes for comparing to humans
df <- cluster.averages$RNA
rownames(df) <- str_to_upper(rownames(df))
write.csv(df, "./Results/Cluster Averages_Cell.Type1/Cell.Type1 Clusters mean RNA expression_CAPS.csv")
df <- cluster.averages$SCT
rownames(df) <- str_to_upper(rownames(df))
write.csv(df, "./Results/Cluster Averages_Cell.Type1/Cell.Type1 Clusters mean SCT expression_CAPS.csv")
rm(df)
Epithelial <- SetIdent(Epithelial, value = Epithelial$Cell.Type2)
Epithelial <- subset(Epithelial,
idents = c("PT",
"DTL",
"ATL",
"TAL",
"DCT",
"PC",
"IC"))
Epithelial@active.ident <- factor(Epithelial@active.ident,
levels=c("PT",
"DTL",
"ATL",
"TAL",
"DCT",
"PC",
"IC"))
DimPlot(object = Epithelial,
reduction = "umap",
#group.by = "Cell.Type2",
repel = T,
label = T)
ggsave(filename = paste("./Results/07_Cell.Type2_UMAP", ".tiff", sep = ""),
device = "tiff",
dpi = 600)
DimPlot(object = Epithelial,
reduction = "tsne",
#group.by = "Cell.Type2",
repel = T,
label = T)
ggsave(filename = paste("./Results/07_Cell.Type2_TSNE", ".tiff", sep = ""),
device = "tiff",
dpi = 600)
DimPlot(object = Epithelial,
reduction = "pca",
#group.by = "Cell.Type",
repel = T,
label = T)
ggsave(filename = paste("./Results/07_Cell.Type2_PCA", ".tiff", sep = ""),
device = "tiff",
dpi = 600)
table(Epithelial$Cell.Type2)
DotPlot(Epithelial,
features = HubMAP_Curated,
assay = "RNA",
cols = c("white", "blue"),
scale = T,
scale.by = "radius",
dot.scale = 6) +
RotatedAxis()
ggsave(filename = paste("./Results/08_DotPlot_Cell.Type2", ".tiff", sep = ""),
bg = "white",
device = "tiff",
dpi = 600)
dir.create(file.path("./Results", "Cluster Markers_Cell.Type2"), showWarnings = T)
DefaultAssay(object = Epithelial) <- "RNA"
markers_list <- list()
# Loop over each cell identity (cluster)
for (clus in unique(Epithelial@active.ident)) {
# Find markers for the current cluster
markers <- FindMarkers(Epithelial,
ident.1 = clus,
logfc.threshold = 1,
min.pct = 0.25,
test.use = "wilcox",
only.pos = FALSE,
max.cells.per.ident = Inf,
random.seed = 1,
verbose = TRUE)
# Add the markers to the list, with the cluster identity as the list name
markers_list[[paste("Cluster", clus, sep = " ")]] <- markers
# Run garbage collection and remove objects from memory
gc(verbose = TRUE)
}
# Write the markers to separate CSV files
lapply(names(markers_list), function(x) {
write.csv(markers_list[[x]],
file = paste0("./Results/Cluster Markers_Cell.Type2/", x, "_Markers.csv"),
row.names = T)
})
dir.create(file.path("./Results", "Cluster Averages_Cell.Type2"), showWarnings = T)
cluster.averages <- AverageExpression(Epithelial)
head(cluster.averages[["RNA"]][1:10,])
write.csv(cluster.averages$RNA, "./Results/Cluster Averages_Cell.Type2/Cell.Type2 Clusters mean RNA expression.csv")
cluster.averages <- AverageExpression(Epithelial)
head(cluster.averages[["SCT"]][1:10,])
write.csv(cluster.averages$SCT, "./Results/Cluster Averages_Cell.Type2/Cell.Type2 Clusters mean SCT expression.csv")
# Write capitalized genes for comparing to humans
df <- cluster.averages$RNA
rownames(df) <- str_to_upper(rownames(df))
write.csv(df, "./Results/Cluster Averages_Cell.Type2/Cell.Type2 Clusters mean RNA expression_CAPS.csv")
df <- cluster.averages$SCT
rownames(df) <- str_to_upper(rownames(df))
write.csv(df, "./Results/Cluster Averages_Cell.Type2/Cell.Type2 Clusters mean SCT expression_CAPS.csv")
rm(df)
Epithelial <- SetIdent(Epithelial, value = Epithelial$Region)
Epithelial <- subset(Epithelial,
idents = c("PT",
"Thin.limbs",
"TAL",
"DCT",
"CD"))
Epithelial@active.ident <- factor(Epithelial@active.ident,
levels=c("PT",
"Thin.limbs",
"TAL",
"DCT",
"CD"))
DimPlot(object = Epithelial,
reduction = "umap",
repel = T,
label = T)
ggsave(filename = paste("./Results/09_Region_UMAP", ".tiff", sep = ""),
device = "tiff",
dpi = 600)
DimPlot(object = Epithelial,
reduction = "tsne",
repel = T,
label = T)
ggsave(filename = paste("./Results/09_Region_TSNE", ".tiff", sep = ""),
device = "tiff",
dpi = 600)
DimPlot(object = Epithelial,
reduction = "pca",
#group.by = "Cell.Type",
repel = T,
label = T)
ggsave(filename = paste("./Results/09_Region_PCA", ".tiff", sep = ""),
device = "tiff",
dpi = 600)
table(Epithelial$Region)
DotPlot(Epithelial,
features = HubMAP_Curated,
assay = "RNA",
cols = c("white", "blue"),
scale = T,
scale.by = "radius",
dot.scale = 6) +
RotatedAxis()
ggsave(filename = paste("./Results/10_DotPlot_Region", ".tiff", sep = ""),
bg = "white",
device = "tiff",
dpi = 600)
dir.create(file.path("./Results", "Cluster Markers_Region"), showWarnings = T)
DefaultAssay(object = Epithelial) <- "RNA"
markers_list <- list()
# Loop over each cell identity (cluster)
for (clus in unique(Epithelial@active.ident)) {
# Find markers for the current cluster
markers <- FindMarkers(Epithelial,
ident.1 = clus,
logfc.threshold = 1,
min.pct = 0.25,
test.use = "wilcox",
only.pos = FALSE,
max.cells.per.ident = Inf,
random.seed = 1,
verbose = TRUE)
# Add the markers to the list, with the cluster identity as the list name
markers_list[[paste("Cluster", clus, sep = " ")]] <- markers
# Run garbage collection and remove objects from memory
gc(verbose = TRUE)
}
# Write the markers to separate CSV files
lapply(names(markers_list), function(x) {
write.csv(markers_list[[x]],
file = paste0("./Results/Cluster Markers_Region/", x, "_Markers.csv"),
row.names = T)
})
dir.create(file.path("./Results", "Cluster Averages_Region"), showWarnings = T)
cluster.averages <- AverageExpression(Epithelial)
head(cluster.averages[["RNA"]][1:10,])
write.csv(cluster.averages$RNA, "./Results/Cluster Averages_Region/Region Clusters mean RNA expression.csv")
cluster.averages <- AverageExpression(Epithelial)
head(cluster.averages[["SCT"]][1:10,])
write.csv(cluster.averages$SCT, "./Results/Cluster Averages_Region/Region Clusters mean SCT expression.csv")
# Write capitalized genes for comparing to humans
df <- cluster.averages$RNA
rownames(df) <- str_to_upper(rownames(df))
write.csv(df, "./Results/Cluster Averages_Region/Region Clusters mean RNA expression_CAPS.csv")
df <- cluster.averages$SCT
rownames(df) <- str_to_upper(rownames(df))
write.csv(df, "./Results/Cluster Averages_Region/Region Clusters mean SCT expression_CAPS.csv")
rm(df)
saveRDS(all.combined, file = "../ALL.assigned_r1.rds")
sessionInfo()