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Eze_Nowa_integration.R
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library(Seurat)
library(SeuratData)
library(patchwork)
library(metap)
library(stringr)
library(ggplot2)
library(ggpubr)
library(tidyr)
library(dplyr)
library(scales)
library(readxl)
library(matrixStats)
`%!in%` <- Negate(`%in%`)
main_outs <- "UCSC/outputs/Eze_Nowakowski_integrated/"
main_deg <- "UCSC/DEGs/Eze_Nowakowski_integrated_2nd_trimester/"
main_scenic <- "UCSC/SCENIC/Eze_Nowakowski_integrated_2nd_trimester/"
input_rds_path <- "UCSC/Seurat_UCSC"
####### Modified tutorial from https://satijalab.org/seurat/articles/integration_introduction.html
input_rds_files <- list.files(path = input_rds_path, pattern = ".rds", full.names = T)[1:2]
input_rds <- lapply(input_rds_files,function(x) {
readRDS(file = x)
})
names(input_rds) <- list.files(path = input_rds_path, pattern = ".rds", full.names = F)[1:2]
names(input_rds) <- str_remove_all(names(input_rds), ".rds")
# normalize and identify variable features for each dataset independently
input_rds <- lapply(X = input_rds, FUN = function(x) {
x <- NormalizeData(x)
x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})
# select features that are repeatedly variable across datasets for integration
features <- SelectIntegrationFeatures(object.list = input_rds)
common.anchors <- FindIntegrationAnchors(object.list = input_rds, anchor.features = features)
# this command creates an 'integrated' data assay
rds.combined <- IntegrateData(anchorset = common.anchors)
# specify that we will perform downstream analysis on the corrected data note that the
# original unmodified data still resides in the 'RNA' assay
DefaultAssay(rds.combined) <- "integrated"
# Run the standard workflow for visualization and clustering
rds.combined <- ScaleData(rds.combined, verbose = FALSE)
rds.combined <- RunPCA(rds.combined, npcs = 30, verbose = FALSE)
rds.combined <- RunUMAP(rds.combined, reduction = "pca", dims = 1:30)
rds.combined <- FindNeighbors(rds.combined, reduction = "pca", dims = 1:30)
rds.combined <- FindClusters(rds.combined, resolution = 0.5)
rds.combined@project.name <- "Eze_Nowakowski_integrated"
# Visualization
DimPlot(rds.combined, reduction = "umap")
DimPlot(rds.combined, reduction = "umap", group.by = "proj")
ggsave(paste0(main_outs, rds.combined@project.name, "_proj.pdf"))
saveRDS(rds.combined, "UCSC/Seurat_UCSC/Eze_Nowakowski_integrated.rds")
#rm(input_rds, input_rds_files, input_rds_path)
####### FIX AGE METADATA
DimPlot(rds.combined, reduction = "umap", group.by = "Age") # Eze
DimPlot(rds.combined, reduction = "umap", group.by = "Carnegie_Stage") # Eze
DimPlot(rds.combined, reduction = "umap", group.by = "Age.Range") # Eze
DimPlot(rds.combined, reduction = "umap", group.by = "sex_age") # shared
DimPlot(rds.combined, reduction = "umap", group.by = "Age_in_Weeks") # Nowakowksi
DimPlot(rds.combined, reduction = "umap", group.by = "Sample.Age..pcw.") # Nowakowksi
rds.combined@meta.data[which(rds.combined@meta.data$Age.Range=="Midlle"),"Age.Range"] <- "Middle"
sex_age_shared <- as.data.frame(table(rds.combined$sex_age))
sex_age_shared <- separate(sex_age_shared, Var1, into=c("sex", "age"), remove = F, sep = "_")
sex_age_shared$age <- as.numeric(sex_age_shared$age)
sex_age_shared$age_round <- round(sex_age_shared$age)
ggplot(sex_age_shared, aes(round(age))) +
geom_histogram(binwidth = 1)
rds.combined@meta.data$age_final <- round(coalesce(rds.combined@meta.data$Age,rds.combined@meta.data$Age_in_Weeks))
DimPlot(rds.combined, reduction = "umap", group.by = "age_final")
ggsave(paste0(main_outs, rds.combined@project.name, "_age_combined.pdf"))
# 1st_trimester: 1 - 12
# 2nd_trimester: 13 - 26
# 3rd_trimester: 27 - 40
rds.combined@meta.data$trimesters <- rep("no_data", nrow(rds.combined@meta.data))
rds.combined@meta.data[which(0 < rds.combined@meta.data$age_final & rds.combined@meta.data$age_final < 13), "trimesters"] <- "1st"
rds.combined@meta.data[which(12 < rds.combined@meta.data$age_final & rds.combined@meta.data$age_final < 27), "trimesters"] <- "2nd"
rds.combined@meta.data[which(26 < rds.combined@meta.data$age_final & rds.combined@meta.data$age_final < 41), "trimesters"] <- "3rd"
DimPlot(rds.combined, reduction = "umap", group.by = "trimesters")
ggsave(paste0(main_outs, rds.combined@project.name, "_trimesters.pdf"))
saveRDS(rds.combined, "UCSC/Seurat_UCSC/Eze_Nowakowski_integrated.rds")
rds.combined@meta.data$sex_age <- paste(rds.combined@meta.data$sex, rds.combined@meta.data$age_final, sep="_")
DimPlot(rds.combined, reduction = "umap", group.by = "sex_age")
rds.combined@meta.data$id <- coalesce(rds.combined@meta.data$Individual, rds.combined@meta.data$Name)
rds.combined@meta.data$id_sex_age <- paste(rds.combined@meta.data$id, rds.combined@meta.data$sex_age, sep="_")
####### NUM OF CELLS BY AGE AND TRIMESTER
num_cells <- as.data.frame(table(rds.combined$id_sex_age))
num_cells <- separate(num_cells, Var1, into=c("id", "sex", "age"), sep="_")
num_cells$age <- as.numeric(num_cells$age)
#trimesters <- data.frame(c(1,12.5,26.5), c(12.5,26.5,40.5), c(hue_pal()(3)))
#colnames(trimesters) <- c("bg_start", "bg_end", "colors")
p1 <- ggplot() +
geom_bar(data = num_cells, aes(age, fill=sex)) +
geom_vline(xintercept = c(12.5, 26.5), linetype = "dashed") +
#geom_rect(data = trimesters, aes(xmin = bg_start,
# xmax = bg_end,
# ymin = - Inf,
# ymax = Inf,
# fill=colors),
# alpha = 0.1) +
scale_y_continuous(breaks= seq(0, nrow(num_cells),by=1)) +
scale_x_continuous(breaks= seq(0, nrow(num_cells),by=1)) +
labs(x="GW Age", y="Number of samples", fill="Sex") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
axis.title.x = element_text(size=12, face="bold", colour = "black"),
axis.text.x = element_text(size=10, colour = "black",angle = 90, vjust = 0.7, hjust=0.5),
axis.title.y = element_text(size=12, face="bold", colour = "black"),
axis.text.y = element_text(size=10, colour = "black",angle = 0, vjust = 0.7, hjust=0.5),
legend.position = "bottom",
legend.title = element_text(size=12, face="bold", colour = "black"),
strip.text = element_text(size=12, face="bold", colour = "black"))
p2 <- ggplot(num_cells, aes(age, Freq, fill=sex)) +
geom_bar(stat="identity", position="dodge") +
scale_x_continuous(breaks= seq(0, nrow(num_cells),by=1)) +
labs(x="GW Age", y="Number of cells/sample", fill="Sex") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
axis.title.x = element_text(size=12, face="bold", colour = "black"),
axis.text.x = element_text(size=10, colour = "black",angle = 90, vjust = 0.7, hjust=0.5),
axis.ticks.x=element_blank(),
axis.title.y = element_text(size=12, face="bold", colour = "black"),
axis.text.y = element_text(size=10, colour = "black",angle = 0, vjust = 0.7, hjust=0.5),
legend.position = "bottom",
legend.title = element_text(size=12, face="bold", colour = "black"),
strip.text = element_text(size=12, face="bold", colour = "black"))
num_cells_int <- ggarrange(p1, p2, common.legend = T, legend = "bottom", nrow = 2)
pdf(paste0(main_outs, "num_samples_and_cells_integrated.pdf"), width = 10)
print(num_cells_int)
dev.off()
rds.combined@meta.data$id_sex_age_trimester <- paste(rds.combined@meta.data$id_sex_age,rds.combined@meta.data$trimesters, sep="_")
num_cells <- as.data.frame(table(rds.combined$id_sex_age_trimester))
num_cells <- separate(num_cells, Var1, into=c("id", "sex", "age", "trimester"), sep="_")
num_cells$age <- as.numeric(num_cells$age)
num_cells$trimester <- factor(num_cells$trimester, c("1st", "2nd", "3rd"))
p1 <- ggplot() +
geom_bar(data = num_cells, aes(trimester, fill=sex), position = "dodge") +
geom_hline(yintercept = c(3), linetype = "dashed") +
#geom_rect(data = trimesters, aes(xmin = bg_start,
# xmax = bg_end,
# ymin = - Inf,
# ymax = Inf,
# fill=colors),
# alpha = 0.1) +
scale_y_continuous(breaks= seq(0, nrow(num_cells),by=1)) +
labs(x="Trimesters", y="Number of samples", fill="Sex") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
axis.title.x = element_text(size=12, face="bold", colour = "black"),
axis.text.x = element_text(size=10, colour = "black",angle = 90, vjust = 0.7, hjust=0.5),
axis.title.y = element_text(size=12, face="bold", colour = "black"),
axis.text.y = element_text(size=10, colour = "black",angle = 0, vjust = 0.7, hjust=0.5),
legend.position = "bottom",
legend.title = element_text(size=12, face="bold", colour = "black"),
strip.text = element_text(size=12, face="bold", colour = "black"))
p2 <- ggplot(num_cells, aes(trimester, Freq, fill=sex)) +
geom_bar(stat="identity", position="dodge") +
labs(x="Trimesters", y="Number of cells/sample", fill="Sex") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
axis.title.x = element_text(size=12, face="bold", colour = "black"),
axis.text.x = element_text(size=10, colour = "black",angle = 90, vjust = 0.7, hjust=0.5),
axis.ticks.x=element_blank(),
axis.title.y = element_text(size=12, face="bold", colour = "black"),
axis.text.y = element_text(size=10, colour = "black",angle = 0, vjust = 0.7, hjust=0.5),
legend.position = "bottom",
legend.title = element_text(size=12, face="bold", colour = "black"),
strip.text = element_text(size=12, face="bold", colour = "black"))
num_cells_trim <- ggarrange(p1, p2, common.legend = T, legend = "bottom", nrow = 2)
pdf(paste0(main_outs, "num_samples_and_cells_integrated_trimester.pdf"), width = 10)
print(num_cells_trim)
dev.off()
# For performing differential expression after integration, we switch back to the original
# data
DefaultAssay(rds.combined) <- "RNA"
VlnPlot(rds.combined, features = "XIST", group.by = "id_sex_age_trimester") + NoLegend()
saveRDS(rds.combined, "UCSC/Seurat_UCSC/Eze_Nowakowski_integrated.rds")
####### CLUSTERS
DimPlot(rds.combined, reduction = "umap", group.by = "Cell.Type") # Eze
DimPlot(rds.combined, reduction = "umap", group.by = "WGCNAcluster") # Nowakowski
### Retrieve number of Eze clusters in each Cell.Type
rds.combined@meta.data[which(is.na(rds.combined@meta.data$Cell.Type)), "Cell.Type"] <- "Nowakowski"
ct_clusters <- list()
ct_names <- vector()
for (ct in unique(rds.combined@meta.data$Cell.Type)) {
if (ct != "Nowakowski") {
ct_names <- c(ct_names, ct)
ct_clusters <- append(ct_clusters, list(as.numeric(unique(rds.combined@meta.data[which(rds.combined@meta.data$Cell.Type==ct), "integrated_snn_res.0.5"]))))
}
}
names(ct_clusters) <- ct_names
Reduce(intersect, ct_clusters)
p1 <- DimPlot(rds.combined, reduction = "umap", group.by = "integrated_snn_res.0.5", split.by = "proj")
p2 <- DimPlot(rds.combined, reduction = "umap", group.by = "Cell.Type", split.by = "proj")
p3 <- DimPlot(rds.combined, reduction = "umap", group.by = "Cell.Type", split.by = "Cell.Type")
eze_clusters <- ggarrange(p1, p2, nrow = 2)
pdf(paste0(main_outs, "Eze_clusters_integrated.pdf"), width = 8)
print(eze_clusters)
dev.off()
pdf(paste0(main_outs, "Eze_clusters_integrated_split_ct.pdf"), width = 25)
print(p3)
dev.off()
eze <- subset(rds.combined, proj=="Eze_2021")
DimPlot(eze, reduction = "umap", group.by = "Cell.Type")
Idents(eze) <- "Cell.Type"
eze_markers <- FindAllMarkers(eze, assay = "RNA", logfc.threshold = 0.25, min.pct = 0.1)
write.csv(eze_markers, paste0(main_outs, "Eze_markers_for_annotation.csv"))
# let's try to recluster nowa
nowa <- subset(rds.combined, proj=="Nowakowski_2017")
DefaultAssay(nowa) <- "integrated"
nowa <- FindNeighbors(nowa, dims = 1:7)
nowa <- FindClusters(nowa, resolution = 0.5)
DimPlot(nowa, reduction = "umap")
for (ct in unique(eze_markers$cluster)) {
ct_markers <- subset(eze_markers, cluster==ct)
top20 <- ct_markers[1:20, "gene"]
print(DotPlot(nowa, features = unique(top20), assay = "RNA") + RotatedAxis())
}
top5 <- vector()
for (ct in unique(eze_markers$cluster)) {
ct_markers <- subset(eze_markers, cluster==ct)
top5 <- c(top5, ct_markers[1:5, "gene"])
}
DotPlot(nowa, features = unique(top5), assay = "RNA") + RotatedAxis()
# From this, it seems that:
# Neuronal: 5
# Mesenchymal: 3, 6
# Radial Glial: 6, 7
# Other: 6
# Neuroepithelial: 7
# IPC: maybe 1
# still not clear..
DimPlot(nowa, reduction = "umap", group.by = "WGCNAcluster")
# empty string is probably U2
nowa@meta.data[which(nowa@meta.data$WGCNAcluster== ""), "WGCNAcluster"] <- "U2"
# try to match each nowa cluster to an eze one
cluster_match <- c(
"IPC-nEN1" = "IPC",
"IPC-div2" = "IPC",
"IPC-div1" = "IPC",
"OPC" = "Other",
"tRG" = "Radial Glial",
"IN-CTX-MGE2" = "Neuronal",
"nEN-late" = "Neuronal",
"nEN-early2" = "Neuronal",
"Mural" = "Other",
"IN-CTX-MGE1" = "Neuronal",
"IN-CTX-CGE2" = "Neuronal",
"RG-div2" = "Radial Glial",
"vRG" = "Radial Glial",
"IPC-nEN2" = "IPC",
"IPC-nEN3" = "IPC",
"IN-CTX-CGE1"= "Neuronal",
"IN-STR" = "Neuronal",
"nIN5" = "Neuronal",
"MGE-IPC3" = "IPC",
"nIN4" = "Neuronal",
"MGE-RG1" = "Radial Glial",
"MGE-RG2" = "Radial Glial",
"MGE-div" = "Other",
"MGE-IPC2" = "IPC",
"MGE-IPC1" = "IPC",
"nIN2" = "Neuronal",
"nIN1" = "Neuronal",
"EN-V1-1" = "Neuronal",
"nIN3" = "Neuronal",
"EN-PFC1" = "Neuronal",
"nEN-early1" = "Neuronal",
"U2" = "Unknown",
"RG-div1" = "Radial Glial",
"EN-PFC2" = "Neuronal",
"EN-PFC3" = "Neuronal",
"EN-V1-2" = "Neuronal",
"RG-early" = "Radial Glial",
"Endothelial" = "Other",
"Astrocyte" = "Other",
"Microglia" = "Other",
"oRG" = "Radial Glial",
"Glyc" = "Other",
"EN-V1-3" = "Neuronal",
"Choroid" = "Other",
"U1" = "Unknown",
"U3" = "Unknown",
"U4"= "Unknown"
)
rds.combined@meta.data$cluster_final <- coalesce(rds.combined@meta.data$Cell.Type, rds.combined@meta.data$WGCNAcluster)
for (nowa_ct in names(cluster_match)) {
rds.combined@meta.data[which(rds.combined@meta.data$WGCNAcluster==nowa_ct), "cluster_final"] <- cluster_match[[nowa_ct]]
}
rds.combined@meta.data[which(rds.combined@meta.data$cluster_final=="Nowakowski"), "cluster_final"] <- "Unknown"
DimPlot(rds.combined, reduction = "umap", group.by = "cluster_final")
saveRDS(rds.combined, "UCSC/Seurat_UCSC/Eze_Nowakowski_integrated.rds")
########################
rds.combined <- readRDS("UCSC/Seurat_UCSC/Eze_Nowakowski_integrated.rds")
DimPlot(rds.combined, reduction = "umap", group.by = "cluster_final")
DimPlot(rds.combined, reduction = "umap", group.by = "cluster_final", split.by = "sex")
ggsave(paste0(main_outs, "clusters_by_sex.pdf"))
other_cl <- subset(rds.combined, cluster_final == "Other")
DefaultAssay(other_cl) <- "integrated"
DimHeatmap(other_cl, dims = 1:15, cells = 500, balanced = TRUE)
other_cl <- JackStraw(other_cl, num.replicate = 100)
other_cl <- ScoreJackStraw(other_cl, dims = 1:20)
JackStrawPlot(other_cl, dims = 1:15)
ElbowPlot(other_cl)
other_cl <- FindNeighbors(other_cl, dims = 1:8)
other_cl <- FindClusters(other_cl, resolution = 0.5)
DimPlot(other_cl, reduction = "umap", group.by = "WGCNAcluster")
extra_path <- "DISCOv1.0/20220817_DEGs/extra_files/"
cm1 <- read.csv(paste0(extra_path, "CellMarker.csv"))
cm2 <- read.csv(paste0(extra_path, "CellMarker-2.csv"))
cm3 <- read.csv(paste0(extra_path, "CellMarker-3.csv"))
cm_df <- rbind(cm1, cm2, cm3)
rm(cm1, cm2, cm3)
cm_df <- cm_df[, -c(6:8)]
col_factors <- c("Species",
"Tissue",
"Cell.Type",
"Cancer")
cm_df[col_factors] <- lapply(cm_df[col_factors], as.factor)
cm_df <- subset(cm_df, subset = (Cancer == "Normal"))
cm_df <- subset(cm_df, subset = (Tissue == c("Brain") | Tissue == c("Dorsolateral prefrontal cortex")))
cm_df$Cancer <- droplevels(cm_df$Cancer)
cm_df$Tissue <- droplevels(cm_df$Tissue)
cm_df$Cell.Type <- droplevels(cm_df$Cell.Type)
ct_list <- c(
"Astrocyte" ="astrocyte",
"B cell" = "B",
"Endothelial cell" = "EC",
"Glial cell" = "Glia" ,
"Glutamatergic neuron" = "EN",
"Interstitial cell" = "IC",
"Lake et al.Science.Ex1" = "EN",
"Lake et al.Science.Ex2" = "EN",
"Lake et al.Science.Ex3" = "EN",
"Lake et al.Science.Ex4" = "EN",
"Lake et al.Science.Ex5" = "EN",
"Lake et al.Science.Ex6" = "EN",
"Lake et al.Science.Ex7" = "EN",
"Lake et al.Science.Ex8" = "EN",
"Lake et al.Science.In1" = "IN",
"Lake et al.Science.In2" = "IN",
"Lake et al.Science.In3" = "IN",
"Lake et al.Science.In4" = "IN",
"Lake et al.Science.In5" = "IN",
"Lake et al.Science.In6" = "IN",
"Lake et al.Science.In7" = "IN",
"Lake et al.Science.In8" = "IN",
"M1 macrophage" = "Macrophage",
"M2 macrophage" = "Macrophage",
"Macrophage"= "Macrophage",
"Microglial cell" = "Microglia",
"Neural progenitor cell" = "NPC",
"Neural stem cell" = "NSC",
"Neuron" = "Neuron",
"Neutrophil" = "Neutrophil",
"Oligodendrocyte" = "Oligodendrocyte",
"Oligodendrocyte precursor cell" = "Oligodendrocyte",
"Oligodendrocyte progenitor cell" = "Oligodendrocyte",
"Pericyte" = "Pericyte",
"Purkinje cell" = "Neuron",
"Stem cell" = "Stem cell",
"T cell" = "T",
"T helper2 (Th2) cell" = "T"
)
cm_df$ct <- rep(NA, nrow(cm_df))
for (ct in levels(cm_df$Cell.Type)) {
cm_df[which(cm_df$Cell.Type==ct), "ct"] <- ct_list[[ct]]
}
cm_df$ct <- as.factor(cm_df$ct)
markers <- list()
for (i in levels(cm_df$ct)) {
markers <- append(markers, list((cm_df[which(cm_df$ct==i),"Cell.Marker"])))
}
names(markers) <- levels(cm_df$ct)
for (ct in names(markers)) {
gene_list <- vector()
for (i in 1:length(markers[[ct]])) {
gene_list <- c(gene_list, str_split(markers[[ct]][i], ", "))
}
markers[[ct]] <- unique(unlist(gene_list))
}
markers <- markers[c("astrocyte", "B", "EC", "Glia", "IC", "Macrophage",
"Microglia", "Neutrophil", "Oligodendrocyte", "Pericyte", "Stem cell",
"T" )]
for (ct in names(markers)) {
DoHeatmap(other_cl,markers[[ct]])
}
DoHeatmap(other_cl,markers[[1]]) # -> 1,3? "astrocyte"
DoHeatmap(other_cl,markers[[2]]) # no features "B"
DoHeatmap(other_cl,markers[[3]]) # -> 8? "EC"
DoHeatmap(other_cl,markers[[4]]) # -> 7? "Glia"
DoHeatmap(other_cl,markers[[5]]) # no features "IC"
DoHeatmap(other_cl,markers[[6]]) # -> 1? "Macrophage"
DoHeatmap(other_cl,markers[[7]]) # -> 8 "Microglia"
DoHeatmap(other_cl,markers[[8]]) # no features "Neutrophil"
DoHeatmap(other_cl,markers[[9]]) # -> all? "Oligodendrocyte"
DoHeatmap(other_cl,markers[[10]]) # no features "Pericyte"
DoHeatmap(other_cl,markers[[11]]) # -> none? "Stem cell"
DoHeatmap(other_cl,markers[[12]]) # no features "T"
# it seems that only 8 can be annotated as microglia
other_cl@meta.data$other_ann <- rep("no_data", nrow(other_cl@meta.data))
other_cl@meta.data[which(other_cl@meta.data$seurat_clusters==8), "other_ann"] <- "Microglia"
nowa_markers <- read_xlsx("UCSC/UCSC_downloads/meta_Nowakowski_2017_suppl.xlsx",
sheet = 5)
nowa_sub <- unique(other_cl$WGCNAcluster)
nowa_sub <- nowa_sub[2:length(nowa_sub)]
nowa_markers <- subset(nowa_markers, cluster %in% nowa_sub)
DoHeatmap(other_cl, unique(nowa_markers$gene)) + RotatedAxis()
saveRDS(rds.combined, "UCSC/Seurat_UCSC/Eze_Nowakowski_integrated.rds")
#####################
rds.combined <- readRDS("UCSC/Seurat_UCSC/Eze_Nowakowski_integrated.rds")
DimPlot(rds.combined, reduction = "umap", group.by = "cluster_final")
other_cl <- subset(rds.combined, cluster_final == "Other")
DefaultAssay(other_cl) <- "integrated"
DimHeatmap(other_cl, dims = 1:15, cells = 500, balanced = TRUE)
other_cl <- JackStraw(other_cl, num.replicate = 100)
other_cl <- ScoreJackStraw(other_cl, dims = 1:20)
JackStrawPlot(other_cl, dims = 1:15)
ElbowPlot(other_cl)
other_cl <- FindNeighbors(other_cl, dims = 1:8)
other_cl <- FindClusters(other_cl, resolution = 0.5)
DimPlot(other_cl, reduction = "umap", group.by = "WGCNAcluster")
expr_mat_all <- GetAssayData(other_cl[["RNA"]], slot="data")
other_cl@meta.data[which(is.na(other_cl@meta.data$WGCNAcluster)), "WGCNAcluster"] <- "Eze"
other_cl@meta.data$seurat_cluster_combo <- paste(other_cl@meta.data$seurat_clusters, other_cl@meta.data$WGCNAcluster, sep = "_")
Idents(other_cl) <- "seurat_cluster_combo"
cell_info <- data.frame()
for (i in unique(other_cl@meta.data$seurat_cluster_combo)) {
print(i)
cell_id <- WhichCells(other_cl, idents = i)
og_group <- rep(i, length(cell_id))
cell_info <- rbind(cell_info, data.frame(cell_id, og_group))
}
cell_info <- separate(cell_info, og_group, into = c("seurat_cluster", "WGCNAcluster"), remove = F, sep = "_")
expr_mat_all <- as.data.frame(as.matrix(expr_mat_all))
expr_mat_all$SD <- rowSds(as.matrix(expr_mat_all))
expr_mat_all <- expr_mat_all[which(expr_mat_all$SD > 0), ]
if (nrow(expr_mat_all) * 0.25 > 2000) {
expr_mat_all <- expr_mat_all[which(expr_mat_all$SD > quantile(expr_mat_all$SD)[4]), ]
} else {
print(" less than 2k genes above third quantile")
}
# order df in descending order
expr_mat_all <- expr_mat_all[order(-expr_mat_all$SD),]
expr_mat_all <- expr_mat_all[1:2000, ]
expr_mat_all <- cbind("Genes" = rownames(expr_mat_all), expr_mat_all)
rownames(expr_mat_all) <- NULL
expr_sums <- colSums(expr_mat_all[2:ncol(expr_mat_all)])
if (identical(length(which(expr_sums>0)), length(expr_sums))) {
print("all columns express at least one gene")
} else {
expr_mat_all <- expr_mat_all[ , !(names(expr_mat_all) %in% which(expr_sums>0))]
print("calculate how many cells have been filtered out")
}
expr_mat_all <- expr_mat_all %>%
relocate(SD, .after = Genes)
#saveRDS(expr_mat_all, "DISCOv1.0/20220817_DEGs/top_2000_SD_expr_matrix.rds")
avg_expr <- data.frame(expr_mat_all[,1])
col_names <- vector()
for (cl_seu in unique(cell_info$seurat_cluster)) {
seu_cells <- cell_info[which(cell_info$seurat_cluster==cl_seu), "cell_id"]
avg_expr <- cbind(avg_expr, rowMeans(expr_mat_all[,seu_cells]))
col_names <- c(col_names, paste0("seurat_", cl_seu))
}
for (cl_WGCNA in unique(cell_info$WGCNAcluster)) {
WGCNA_cells <- cell_info[which(cell_info$WGCNAcluster==cl_WGCNA), "cell_id"]
avg_expr <- cbind(avg_expr, rowMeans(expr_mat_all[,WGCNA_cells]))
col_names <- c(col_names, cl_WGCNA)
}
colnames(avg_expr) <- c("Genes", col_names)
rownames(avg_expr) <- avg_expr$Genes
avg_expr[,1] <- NULL
cor_pearson <- cor(avg_expr, method = "pearson")
p.mat <- cor_pmat(avg_expr)
library(ggcorrplot)
ggcorrplot(cor_spearman, p.mat = p.mat, type = "lower", lab = T)
ggsave(paste0(main_outs, "other_cluster_correlation_mtx.pdf"))
# the only somewhat stronger correlation is between Seurat_8 and microglia, which is what we had observed previously
other_cl@meta.data$other_ann <- rep(NA, nrow(other_cl@meta.data))
other_cl@meta.data[which(other_cl@meta.data$seurat_clusters==8), "other_ann"] <- "Microglia"
other_cl@meta.data$other_ann <- coalesce(other_cl@meta.data$other_ann, other_cl@meta.data$WGCNAcluster)
DimPlot(other_cl, reduction = "umap", group.by = "other_ann")
Idents(other_cl) <- "other_ann"
microglia_cells <- WhichCells(other_cl, idents = "Microglia")
rds.combined@meta.data[which(rds.combined@meta.data$Cell %in% microglia_cells), "cluster_final"] <- "Microglia"
saveRDS(rds.combined, "UCSC/Seurat_UCSC/Eze_Nowakowski_integrated.rds")
rds.combined@meta.data$id_sex_age_cluster <- paste(rds.combined@meta.data$id_sex_age, rds.combined@meta.data$cluster_final, sep="_")
rds.combined@meta.data$proj_id_sex_age_cluster <- paste(rds.combined@meta.data$proj, rds.combined@meta.data$id_sex_age_cluster, sep="_")
rds.combined@meta.data$id_sex_trim_cluster <- paste(rds.combined@meta.data$id_sex_age_trimester, rds.combined@meta.data$cluster_final, sep="_")
rds.combined@meta.data$proj_id_sex_trim_cluster <- paste(rds.combined@meta.data$proj, rds.combined@meta.data$id_sex_trim_cluster, sep="_")
num_cells <- as.data.frame(table(rds.combined$proj_id_sex_trim_cluster))
num_cells <- separate(num_cells, Var1, into=c("proj","year", "id", "sex", "age", "trim", "cluster"), sep="_")
num_cells$proj <- paste(num_cells$proj, num_cells$year, sep="_")
num_cells$year <- NULL
num_cells$age <- as.numeric(num_cells$age)
num_cells_sex_ct <- ggplot(num_cells, aes(cluster, Freq, fill=sex)) +
geom_bar(stat="identity", position="dodge") +
labs(x="Cell types", y="Number of cells/sample", fill="Sex") +
facet_wrap(~trim, scales = "free") +
geom_hline(yintercept = 500, linetype="dashed") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
axis.title.x = element_text(size=12, face="bold", colour = "black"),
axis.text.x = element_text(size=10, colour = "black",angle = 90, vjust = 0.7, hjust=0.5),
axis.ticks.x=element_blank(),
axis.title.y = element_text(size=12, face="bold", colour = "black"),
axis.text.y = element_text(size=10, colour = "black",angle = 0, vjust = 0.7, hjust=0.5),
legend.position = "bottom",
legend.title = element_text(size=12, face="bold", colour = "black"),
strip.text = element_text(size=12, face="bold", colour = "black"))
pdf(paste0(main_outs, "num_cells_ct_trim_sex.pdf"))
print(num_cells_sex_ct)
dev.off()
########################
rds.combined <- readRDS("UCSC/Seurat_UCSC/Eze_Nowakowski_integrated.rds")
DimPlot(rds.combined, reduction = "umap", group.by = "cluster_final")
DimPlot(rds.combined, reduction = "umap", group.by = "trimesters")
trim_2nd <- subset(rds.combined, trimesters=="2nd")
rm(rds.combined)
DefaultAssay(trim_2nd) <- "integrated"
DimHeatmap(trim_2nd, dims = 1:15, cells = 500, balanced = TRUE)
trim_2nd <- JackStraw(trim_2nd, num.replicate = 100)
trim_2nd <- ScoreJackStraw(trim_2nd, dims = 1:20)
JackStrawPlot(trim_2nd, dims = 1:15)
ElbowPlot(trim_2nd)
trim_2nd <- FindNeighbors(trim_2nd, dims = 1:13)
trim_2nd <- FindClusters(trim_2nd, resolution = 0.5)
DimPlot(trim_2nd, reduction = "umap", group.by = "cluster_final")
DimPlot(trim_2nd, reduction = "umap", group.by = "trimesters")
saveRDS(trim_2nd, "UCSC/Seurat_UCSC/Eze_Nowakowski_integrated_2nd_trimester.rds")
########################
trim_2nd <- readRDS("UCSC/Seurat_UCSC/Eze_Nowakowski_integrated_2nd_trimester.rds")
trim_2nd@meta.data$sex_ct <- paste(trim_2nd@meta.data$sex, trim_2nd@meta.data$cluster_final, sep="_")
trim_2nd@project.name <- "Eze_Nowakowski_integrated_2nd_trimester"
num_sex_ct <- as.data.frame(table(trim_2nd$sex_ct))
num_sex_ct <- separate(num_sex_ct, Var1, into = c("sex" , "ct"), sep = "_", remove = F)
names(num_sex_ct)[names(num_sex_ct) == 'Var1'] <- "idents"
names(num_sex_ct)[names(num_sex_ct) == 'Freq'] <- "count"
col_factors <- c("idents", "sex","ct")
num_sex_ct[col_factors] <- lapply(num_sex_ct[col_factors], as.factor)
FiltDF <- function(df, min_num_cells) {
`%!in%` <- Negate(`%in%`)
df <- droplevels(df)
incomplete_ct <- vector()
for (type in levels(df$ct)) {
if ((nrow(subset(df, subset = ct==type))%%2!=0) | (any(subset(df, subset = ct==type)[,"count"] < min_num_cells))) {
incomplete_ct <- c(incomplete_ct, type)
}
}
df_filt <- df[df$ct %!in% incomplete_ct,]
return(df_filt)
}
min_num_cells <- c(10,50,100)
trim_2nd_output <- paste0(main_deg, trim_2nd@project.name, "/outputs")
dir.create(trim_2nd_output, recursive = T, showWarnings = F)
for (min_cells in min_num_cells) {
num_filt <- FiltDF(num_sex_ct, min_cells)
write.csv(num_filt, file = paste0(trim_2nd_output, "/final_filt_", min_cells, ".csv"),
row.names = F)
pdf(paste0(trim_2nd_output, "/filt_counts_", min_cells, ".pdf"), 10, 15)
print(ggplot(num_sex_ct, aes(ct, count, fill=sex)) +
geom_bar(stat="identity", position = "dodge") +
labs(x="", y="Nuclei count", fill="Sex") +
geom_hline(yintercept = min_cells, linetype="dashed") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
axis.title.x = element_text(size=12, face="bold", colour = "black"),
axis.text.x = element_text(size=8, colour = "black",angle = 45, vjust = 0.5, hjust=0.5),
axis.ticks.x=element_blank(),
axis.title.y = element_text(size=12, face="bold", colour = "black"),
legend.position = "bottom"))
dev.off()
}
saveRDS(trim_2nd, paste0(input_rds_path, "/Eze_Nowakowski_integrated_2nd_trimester.rds"))
############ For 02C_Conservation
trim_2nd <- readRDS(paste0(input_rds_path, "/Eze_Nowakowski_integrated_2nd_trimester.rds"))
Idents(trim_2nd) <- "sex_ct"
expr_mat_all_cts <- GetAssayData(trim_2nd[["RNA"]], slot="data")
cell_info <- data.frame()
for (i in unique(trim_2nd@meta.data$sex_ct)) {
print(i)
cell_id <- WhichCells(trim_2nd, idents = i)
og_group <- rep(i, length(cell_id))
cell_info <- rbind(cell_info, data.frame(cell_id, og_group))
}
write.csv(cell_info, paste0(main_deg, "cell_info.csv"))
rm(trim_2nd)
expr_mat_all_cts <- as.data.frame(as.matrix(expr_mat_all_cts))
df_list <- list()
df_list_n <- vector()
for (df_id in unique(cell_info$og_group)) {
og_cells <- c("Genes", cell_info[which(cell_info$og_group==df_id), "cell_id"])
df_og_df <- expr_mat_all_cts[ , (names(expr_mat_all_cts) %in% og_cells)]
df_list <- append(df_list, list(df_og_df))
df_list_n <- c(df_list_n, df_id)
}
names(df_list) <- df_list_n
FiltDf <- function(df_list_dis) {
filt_names <- vector()
df_dis <- list()
for (k in names(df_list_dis)) {
if (!is.null(ncol(df_list_dis[[k]]))) {
df_dis <- append(df_dis, list(rownames(df_list_dis[[k]][which(rowSums(as.matrix(df_list_dis[[k]]))!=0),])))
filt_names <- c(filt_names, k)
}
}
names(df_dis) <- filt_names
cts <- vector()
genes <- vector()
for (id in names(df_dis)) {
cts <- c(cts, rep(id, length(df_dis[[id]])))
genes <- c(genes, df_dis[[id]])
}
tot_genes <- data.frame(cts, genes)
return(tot_genes)
}
tot_df <- FiltDf(df_list)
tot_df <- separate(tot_df, cts, into=c("sex", "ct"), sep ="_", remove = FALSE)
colnames(tot_df)
names(tot_df)[names(tot_df) == "cts"] <- "og"
col_factors <- c("og", "sex","ct")
tot_df[col_factors] <- lapply(tot_df[col_factors], as.factor)
sexes <- vector()
cts <- vector()
genes <- vector()
for (sex_id in levels(tot_df$sex)) {
for (ct_id in levels(tot_df$ct)) {
common_genes <- tot_df[which(tot_df$sex==sex_id & tot_df$ct==ct_id), "genes"]
genes <- c(genes, common_genes)
sexes <- c(sexes, rep(sex_id, length(common_genes)))
cts <- c(cts, rep(ct_id, length(common_genes)))
}
}
tot_genes <- as.data.frame(cbind(sexes, cts, genes))
colnames(tot_genes) <- c("sex", "ct", "genes")
col_factors <- c("sex", "ct")
tot_genes[col_factors] <- lapply(tot_genes[col_factors], as.factor)
write.csv(tot_genes, paste0(main_deg, "tot_genes_ct.csv"))
############ For SCENIC - HIGHEST VARIABLE GENES
trim_2nd <- readRDS(paste0(input_rds_path, "/Eze_Nowakowski_integrated_2nd_trimester.rds"))
trim_2nd@meta.data$sample_id <- rownames(trim_2nd@meta.data)
trim_2nd@meta.data$sex_ct_sample <- paste(trim_2nd@meta.data$sex_ct, trim_2nd@meta.data$sample_id, sep="_")
Idents(trim_2nd) <- "sex_ct_sample"
expr_mat_all <- GetAssayData(trim_2nd[["RNA"]], slot="data")
cell_info <- read.csv(paste0(main_deg, "cell_info.csv"))
cell_info$X <- NULL
saveRDS(trim_2nd, paste0(input_rds_path, "/Eze_Nowakowski_integrated_2nd_trimester.rds"))
rm(trim_2nd)
expr_mat_all <- as.data.frame(as.matrix(expr_mat_all))
expr_mat_all$SD <- rowSds(as.matrix(expr_mat_all))
expr_mat_all <- expr_mat_all[which(expr_mat_all$SD > 0), ]
if (nrow(expr_mat_all) * 0.25 > 2000) {
expr_mat_all <- expr_mat_all[which(expr_mat_all$SD > quantile(expr_mat_all$SD)[4]), ]
} else {
print(" less than 2k genes above third quantile")
}
# order df in descending order
expr_mat_all <- expr_mat_all[order(-expr_mat_all$SD),]
expr_mat_all <- expr_mat_all[1:2000, ]
expr_mat_all <- cbind("Genes" = rownames(expr_mat_all), expr_mat_all)
rownames(expr_mat_all) <- NULL
expr_sums <- colSums(expr_mat_all[2:ncol(expr_mat_all)])
if (identical(length(which(expr_sums>0)), length(expr_sums))) {
print("all columns express at least one gene")
} else {
expr_mat_all <- expr_mat_all[ , !(names(expr_mat_all) %in% which(expr_sums>0))]
print("calculate how many cells have been filtered out")
}
expr_mat_all <- expr_mat_all %>%
relocate(SD, .after = Genes)
dir.create(main_scenic, showWarnings = F, recursive = T)
saveRDS(expr_mat_all, paste0(main_scenic, "top_2000_SD_expr_matrix.rds"))
##### Map the samples back to the groups they belong to
expr_mat_all <- readRDS(paste0(main_scenic, "top_2000_SD_expr_matrix.rds"))
group_list <- list()
group_list_n <- vector()
for (group_id in unique(cell_info$og_group)) {
og_cells <- c("Genes", cell_info[which(cell_info$og_group==group_id), "cell_id"])
df_og_group <- expr_mat_all[ , (names(expr_mat_all) %in% og_cells)]
group_list <- append(group_list, list(df_og_group))
group_list_n <- c(group_list_n, group_id)
}
names(group_list) <- group_list_n
remove_dfs <- function(df_list, threshold) {
incomplete_dfs <- vector()
for (group_id in names(df_list)) {
if (ncol(df_list[[group_id]]) < (threshold + 1)) {
incomplete_dfs <- c(incomplete_dfs, group_id)
}
}
add_counterpart <- vector()
for (i in incomplete_dfs) {
if (grepl("F", i)) {
m_id <- str_replace(i, "F", "M")
if (m_id %!in% incomplete_dfs) {
add_counterpart <- c(add_counterpart, m_id)
}
} else {
f_id <- str_replace(i, "M", "F")
if (f_id %!in% incomplete_dfs) {
add_counterpart <- c(add_counterpart, f_id)
}
}
}
incomplete_dfs <- c(incomplete_dfs, add_counterpart)
for (i in incomplete_dfs) {
df_list[[i]] <- NULL
}
return(df_list)
}
group_list100 <- remove_dfs(group_list, 100)
group_list500 <- remove_dfs(group_list, 500)
plot_group_numbers <- function(df_list, thresh) {
ids <- as.data.frame(names(df_list))
colnames(ids) <- c("groups")
ids <- separate(ids, groups, into = c("sex","ct"), sep="_", remove=FALSE)
col_factors <- c("sex","ct")
ids[col_factors] <- lapply(ids[col_factors], as.factor)
ids$length_groups <- sapply(1:length(names(df_list)), function(i) ncol(df_list[[i]]))
pdf(paste0(main_scenic, "num_filt_", thresh, "_cells.pdf"))
print(
ggplot(ids, aes(ct, length_groups, fill=sex)) +
geom_bar(stat="identity", position = "dodge") +
geom_hline(yintercept = thresh, linetype="dashed", color = "black") +
labs(title = paste0("Filter: ", thresh, " cells"), x="cell types", y="# of cells", fill="sex") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
axis.title.x = element_text(size=12, face="bold", colour = "black"),
axis.text.x = element_text(size=8, colour = "black",angle = 90, vjust = 0.5, hjust=0.5),
axis.ticks.x=element_blank(),
axis.title.y = element_text(size=12, face="bold", colour = "black"),
legend.title = element_text(size=12, face="bold", colour = "black"),
legend.position = "bottom",
plot.title = element_text(size=12, face="bold", colour = "black"))
)
dev.off()
}
plot_group_numbers(group_list100, 100)
plot_group_numbers(group_list500, 500)
###### Create Randomly sampled dfs
expr_mat_all <- readRDS(paste0(main_scenic, "top_2000_SD_expr_matrix.rds"))
cell_info <- read.csv(paste0(main_deg, "cell_info.csv"))
cell_info$X <- NULL
remove_dfs <- function(df_list, threshold) {
incomplete_dfs <- vector()
for (group_id in names(df_list)) {
if (ncol(df_list[[group_id]]) < (threshold + 1)) {
incomplete_dfs <- c(incomplete_dfs, group_id)
}
}
add_counterpart <- vector()
for (i in incomplete_dfs) {
if (grepl("F", i)) {
m_id <- str_replace(i, "F", "M")
if (m_id %!in% incomplete_dfs) {
add_counterpart <- c(add_counterpart, m_id)
}
} else {
f_id <- str_replace(i, "M", "F")
if (f_id %!in% incomplete_dfs) {
add_counterpart <- c(add_counterpart, f_id)
}
}
}
incomplete_dfs <- c(incomplete_dfs, add_counterpart)
for (i in incomplete_dfs) {
df_list[[i]] <- NULL
}
return(df_list)
}
df_list <- list()
df_list_n <- vector()
for (df_id in unique(cell_info$og_group)) {
og_cells <- c("Genes", cell_info[which(cell_info$og_group==df_id), "cell_id"])
df_og_df <- expr_mat_all[ , (names(expr_mat_all) %in% og_cells)]
df_list <- append(df_list, list(df_og_df))
df_list_n <- c(df_list_n, df_id)
}
names(df_list) <- df_list_n
df_list100 <- df_list
df_list100 <- remove_dfs(df_list100, 100)
check_dfs <- function(group_list) {
remove_groups <- vector()
if (length(group_list) %% 2 != 0 ) {
f_list <- vector()
m_list <- vector()
for (i in names(group_list)) {
if (grepl("F", i)) {
gen <- str_remove(i, "F_")
f_list <- c(f_list, gen)
} else {
gen <- str_remove(i, "M_")
m_list <- c(m_list, gen)
}
}
if (identical(m_list, f_list) == FALSE) {
if (length(m_list) > length(f_list)) {
remove_groups <- c(m_list[which(m_list %!in% f_list)], "M")
} else {
remove_groups <- c(f_list[which(f_list %!in% m_list)], "F")
}
}