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CommsBio_Su_et.al.R
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CommsBio_Su_et.al.R
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################################################################################
################################################################################
#### ####
#### Single-Cell Analysis ####
#### ####
################################################################################
################################################################################
set.seed(11051991)
#### Load library ####
library(Seurat)
library(SeuratWrappers)
library(dplyr)
library(ggplot2)
library(gprofiler2)
library(stringr)
library(RColorBrewer)
library(scales)
library(ggpubr)
library(ggdendro)
library(cowplot)
library(magick)
library(purrr)
library(raster)
library(EnhancedVolcano)
library(gridExtra)
library(grid)
library(gridtext)
library(gtable)
library(officer)
library(ggrastr)
library(ggvenn)
library(monocle3)
library(NMF)
library(snow)
library(survival)
library(survminer)
#### Common input ####
object.name <- c("Ben", "Low1", "Low2", "Med", "High_2", "High_1", "COS")
file.name <- c("L49", "L07", "L28", "L63", "L31", "L44", "L43")
# Standard intigration by MNN
mnnCT7.raw.pc <- 50
mnnCT7.raw.dims <- 40
mnnCT7.raw.res <- 0.2
mnnCT7.pc <- 50
mnnCT7.dims <- 40
mnnCT7.res <- 0.2
# Standard MNN reintegration
mnnCT7.3.pc <- 50
mnnCT7.3.dims <- 40
mnnCT7.3.res <- 0.2
#### Color vector ####
CT7.cluster <- c("Chon1", "High2", "Stro", "Cos", "High1", "Chon2", "Leuk", "Prol")
names(CT7.cluster) <- 0:7
CT7.cluster.names <- c(c(0:7), CT7.cluster)
col.CT7 <- c(brewer.pal(length(CT7.cluster), "Set1"), brewer.pal(length(CT7.cluster), "Set1"))
names(col.CT7) = CT7.cluster.names
col.CT7
col.CT7.old <- col.CT7
col.CT7[c("5", "Chon2")] <- "#FEE715FF"
# Subcluster
mnnCT7.sucluster <- c("Chon1", "Chon2", "High1a", "High1b", "High2a", "High2b",
"High1", "High2", "Cos", "nStro", "Prol", "Stro1", "Leuk")
# Suclusters
col.sub <- c(`High2a`="royalblue", `High2b`="skyblue", `Hype1`="purple", `Hype2`="slateblue",
`High1a`="orangered", `High1b`="orange", `nStro` = "green", col.CT7, `Stro1` = col.CT7[["Stro"]])
# CNV cluster
cluster.cnv <- c("Chon1", "Chon2", "High1", "High2", "Cos", "Prol", "Stro1", "nStro", "Leuk")
col.allcnv <- c(col.CT7, col.sub)[cluster.cnv]
# Patient
patient <- c("Ben", "Low_1", "Low_2", "Med", "High_2", "High_1", "COS")
col.patient <- brewer.pal(length(patient), "Set2")
names(col.patient) = patient
col.patient
# All colors
col.all <- c(col.sub, col.patient)
# Subcluster 2
CT7.2.cluster.id <- c(0:6)
col.CT7.2 <- brewer.pal(length(CT7.2.cluster.id), "Dark2")
names(col.CT7.2) = CT7.2.cluster.id
col.CT7.2
# Stromal cell type cluster
mnnCT7.2.cluster <- setNames(c("miStro", "osStro", "gCAF", "FC", "CAF", "vSMC", "EC"), 0:6)
col.CT7.2 <- c(col.CT7.2, col.CT7.2)
names(col.CT7.2)[1:length(mnnCT7.2.cluster)] <- mnnCT7.2.cluster
col.CT7.2
# Subcluster 6
CT7.6.cluster.id <- c(0:4)
col.CT7.6 <- brewer.pal(length(CT7.6.cluster.id), "Set3")
names(col.CT7.6) = CT7.6.cluster.id
col.CT7.6
# Immune cell type cluster
mnnCT7.6.cluster <- c("M1", "M2", "TC", "SP", "OC")
col.CT7.6 <- c(col.CT7.6, col.CT7.6)
names(col.CT7.6)[1:length(mnnCT7.6.cluster)] <- mnnCT7.6.cluster
col.CT7.6
# Fetal femur
FF.cluster <- setNames(c("TC", "RC", "HC", "PC", "fibroblast", "endothelial cell"), 0:5)
FF.cluster <- FF.cluster[c(2,1,3:6)]
col.ff <- scales::hue_pal()(length(FF.cluster))
col.ff <- c(col.ff, col.ff)
names(col.ff) <- c(FF.cluster, names(FF.cluster))
col.ff
# Histology
his <- c("benign", "low", "medium", "high", "dedifferentiated")
his.col <- brewer.pal(6, "Spectral")[5:1]
names(his.col) <- his
his.col
# Mutation col
mut <- c("TRUE", "FALSE", "#N/A")
mut.col <- c("red", "green", "gray")
names(mut.col) <- mut
mut.col
# Bulk group col
group.name <- c("5" = "Ben_bulk", "3" = "Med_bulk", "4" = "High_bulk", "2" = "Ded_bulk" , "1" = "Stro_bulk")
col.group <- brewer.pal(length(group.name), "Dark2")
names(col.group) <- group.name
col.group
# Heatmap col
ann_colors <- list(COL2A1.mut = mut.col, P53.mutation = mut.col, IDH.mut = mut.col,
histology = his.col, group = col.group)
# Chromosome color
col.chr <- c(brewer.pal(8, "Pastel1")[-6], brewer.pal(8, "Pastel2")[-6], brewer.pal(9, "Set3")[-2], "#000000")
names(col.chr) <- c(1:22, "anno")
# col.chr[-23] <- adjustcolor(col.chr[-23], alpha.f = 0.5)
col.chr
# CNV group color
group.cnv <- c("Norm", "Low", "Med", "High")
col.cnv <- brewer.pal(6, "Spectral")[5:2]
names(col.cnv) <- group.cnv
col.cnv
# NEW color
NEW.cluster <- c(paste0("L80-", 1:3), paste0("L81-", c(1,3,2)), paste0("L96-", 1:5), "Stro", "Leuk")
col.NEW <- c(brewer.pal(length(NEW.cluster)-2, "Set3"), col.CT7[c("Stro", "Leuk")] )
names(col.NEW) <- NEW.cluster
col.NEW
#### Functions ####
#### My Heatmap
library(Seurat)
library(ggplot2)
library(ComplexHeatmap)
library(dplyr)
my_heatmap <- function(object, genes, bar = c("seurat_clusters", "orig.ident"),
row_split = NULL,
column_title = NULL,
row_title = NULL,
show_rownames = T,
show_annotation_name = F,
right_annotation = NULL,
cols = list("x" = setNames("x", "x")),
min = -2.5, max = 2.5, fontsize = 12, res = 1){
mdata <- GetAssayData(object, slot = "scale.data")[genes, ]
# mdata <- FetchData(object, genes, slot = "scale.data") %>% t
# Scale min and max values
pmed <- function(x) pmin(pmax(x, min), max)
mdata <- apply(mdata, 2, pmed)
# Order cells according to input features
if(length(bar) == 1) {
bar_col <- [email protected][,c(bar, bar)]
bar_col <- bar_col[do.call(order, bar_col), ][1]
}else {
bar_col <- [email protected][, bar]
bar_col <- bar_col[do.call(order, bar_col), ]}
mdata <- mdata[ , rownames(bar_col)]
# # Add gaps_col
# gaps_col <- bar_col %>% group_by(bar_col[, 1]) %>% tibble::rowid_to_column("rownames") %>%
# top_n(1, rownames) %>% pull(rownames)
# Heatmap annotation
anno_top <- HeatmapAnnotation(df = bar_col, col = cols,
show_annotation_name = show_annotation_name,
annotation_name_gp = gpar(fontsize = (fontsize-2)),
simple_anno_size = unit(3, "mm"),
annotation_legend_param = list(title_gp = gpar(fontsize = (fontsize-2), fontface = "bold"),
labels_gp = gpar(fontsize = (fontsize-2)),
grid_height = unit(3.5, "mm"),
grid_width = unit(3.5, "mm")))
Heatmap(mdata,
heatmap_legend_param = list(title = "Z-Score",
title_position = "leftcenter-rot",
title_gp = gpar(fontsize = (fontsize-4), fontface = "bold"),
labels_gp = gpar(fontsize = (fontsize-2)),
grid_height = unit(fontsize-7, "mm"),
grid_width = unit(1, "mm")),
top_annotation = anno_top,
right_annotation = right_annotation,
col = c("violet", "black", "yellow"),
row_title_gp = gpar(fontsize = fontsize),
column_title_gp = gpar(fontsize = fontsize, fontface = "bold"),
row_names_gp = gpar(fontsize = (fontsize-2)),
column_names_gp = gpar(fontsize = (fontsize-2)),
column_title = column_title,
row_title = row_title,
column_split = bar_col[, 1],
row_split = row_split,
row_gap = unit(0.1, "pt"),
cluster_rows = FALSE, cluster_columns = FALSE,
show_column_names = F,
show_row_names = F,
use_raster = T,
raster_device = "CairoPNG",
raster_quality = res)
}
#### Other functions
vln <- function(object = mnnCT7, gene = gene, col = NULL, group.by = NULL){
# single vln plot
vln.single <- function(gene = NULL, object = NULL, col = NULL, group.by = NULL) {
VlnPlot(object, gene, col, pt.size = 0, group.by = group.by) + NoLegend() + xlab(NULL) +
theme(axis.title.y = element_text(size = 10)) +
theme(plot.title = element_text(size = 12)) +
theme(axis.text.x = element_text(size = 10))
}
# Check input genes
gene.check <- gene %in% rownames(object)
if(0 %in% gene.check) message("Warning: ", paste0(gene[!gene.check], collapse = ","), " are not in features")
gene <- gene[gene.check]
if(S4Vectors::isEmpty(gene)) break
# Ploting
gene.list <- as.list(gene)
vln.list <- lapply(gene.list, vln.single, object, col, group.by)
grid.arrange(grobs = vln.list, ncol = 1)
}
# Permutation function
FDR.test <- function(v1, v2, nperm){
cosi <- lsa::cosine(v1, v2)
cosis <- sapply(1:nperm, function(x) lsa::cosine(v1, sample(v2, length(v2))))
return(mean(cosis > as.numeric(cosi)))
}
# Box plots for gene expression
bplt <- function(gene = NULL, object = NULL, cluster = "group", ncol = NULL, nrow = NULL, col = NULL, combine = T){
# Single box plot function
bplt.single <- function(gene = NULL, object = NULL, cluster = "group", col = NULL) {
crat_expr <- Biobase::exprs(object)[gene, ]
crat_data <- as.data.frame(crat_expr)
colnames(crat_data)[1] <- "org_value"
crat_data <- cbind(crat_data, Biobase::pData(object)[cluster])
if(is.null(col)) col <- scales::hue_pal()(Biobase::pData(bulk)[, cluster] %>% unique() %>% length())
ggplot(data = crat_data, aes(x = !!ensym(cluster), y = org_value, fill = !!ensym(cluster))) +
theme_classic() +
geom_boxplot() +
scale_fill_manual(values = col)+
ylab("log2(intensity)") +
xlab(cluster) +
ggtitle(gene) +
theme(plot.title = element_text(hjust=0.5)) +
NoLegend()
}
# Check input genes
gene.check <- gene %in% rownames(object)
if(0 %in% gene.check) message("Warning: ", paste0(gene[!gene.check], collapse = ","), " are not in features")
gene <- gene[gene.check]
if(isEmpty(gene)) break
# Ploting
gene.list <- as.list(gene)
plt.list <- lapply(gene.list, bplt.single, object = object, cluster = cluster, col = col)
if(combine == F) {return(plt.list)} else {
if(is.null(ncol) && is.null(nrow)) nrow <- sqrt(length(gene))%/%1
grid.arrange(grobs = plt.list, ncol = ncol, nrow = nrow)
}
}
############################################
#### Batch effects correction using MNN ####
############################################
#### Read and preprocess 10x data ####
data.list <- list()
for (i in 1:length(object.name)) {
print(object.name[i])
data.10x <- Read10X(paste("./count.v3/", file.name[i], sep = ""))
colnames(data.10x) <- paste(colnames(data.10x), object.name[i], sep = ".")
data.list[[i]] <- CreateSeuratObject(counts = data.10x, project = object.name[i], min.cells = 3, min.features = 200)
data.list[[i]][["percent.mt"]] <- PercentageFeatureSet(object = data.list[[i]], pattern = "^MT-")
data.list[[i]][["percent.rp"]] <- PercentageFeatureSet(object = data.list[[i]], pattern = "^RP")
# data.list[[i]] <- subset(x = data.list[[i]], features = grep("^RP", rownames(data.list[[i]]), value = T, invert = T))
data.list[[i]] <- subset(x = data.list[[i]], subset = nFeature_RNA > 200 & nFeature_RNA < 5500 & percent.mt < 5)
data.list[[i]] <- NormalizeData(data.list[[i]], verbose = FALSE)
data.list[[i]] <- FindVariableFeatures(data.list[[i]], selection.method = "vst",
nfeatures = 5000, verbose = FALSE)
}
names(data.list) <- object.name
data.list
#### MNN Integratoin ####
# mnnCT7.raw.pc <- 50, mnnCT7.raw.dims <- 40, resolution = 0.2
mnnCT7.raw <- merge(data.list[[1]], data.list[-1])
mnnCT7.raw <- NormalizeData(mnnCT7.raw)
mnnCT7.raw <- FindVariableFeatures(mnnCT7.raw)
mnnCT7.raw.pc <- 50
mnnCT7.raw <- RunFastMNN(object.list = SplitObject(mnnCT7.raw, split.by = "orig.ident"), d = mnnCT7.raw.pc)
mnnCT7.raw.dims <- 40
mnnCT7.raw.res <- 0.2
mnnCT7.raw <- RunUMAP(mnnCT7.raw, reduction = "mnn", dims = 1:mnnCT7.raw.dims)
mnnCT7.raw <- FindNeighbors(mnnCT7.raw, reduction = "mnn", dims = 1:mnnCT7.raw.dims)
mnnCT7.raw <- FindClusters(mnnCT7.raw, resolution = mnnCT7.raw.res)
DimPlot(mnnCT7.raw, group.by = c("orig.ident", "ident"))
DimPlot(mnnCT7.raw, split.by = c("orig.ident"), group.by = "ident", label = T, ncol = 4, cols = col.CT7)
table(mnnCT7.raw$seurat_clusters)
#### Add tumor grade, clusters, and patients to meta data ####
# Add tumor grade infromation
mnnCT7.raw$histology <- str_split([email protected]$orig.ident, "_") %>% sapply("[[", 1)
mnnCT7.raw$cartilage <- ifelse([email protected]$orig.ident %in% c("Ben", "Low1", "Low2", "Med"),
"TURE", "FALSE")
mnnCT7.raw$patient <- factor(mnnCT7.raw$orig.ident, levels = patient)
mnnCT7.raw$cluster.id <- mnnCT7.raw$seurat_clusters
mnnCT7.raw$cluster <- factor(CT7.cluster[mnnCT7.raw$seurat_clusters], levels = CT7.cluster[c(0,5,4,1,3,7,2,6)+1])
DimPlot(mnnCT7.raw, group.by = c("patient", "cluster"), cols = c(col.patient, col.CT7))
#### Finding differentially expressed features ####
DefaultAssay(mnnCT7.raw) <- "RNA"
mnnCT7.raw <- ScaleData(mnnCT7.raw, features = rownames(mnnCT7.raw))
mnnCT7.raw.markers <- FindAllMarkers(object = mnnCT7.raw, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
mnnCT7.raw.markers <- subset(mnnCT7.raw.markers, p_val_adj < 0.05 & (pct.1 - pct.2) > 0)
table(mnnCT7.raw.markers$cluster)
# write.csv(mnnCT7.raw.markers, paste("./integration/mnnCT7.raw.markers.pc", mnnCT7.raw.pc, ".d", mnnCT7.raw.dims,
# ".r", mnnCT7.raw.res, ".csv", sep = ""))
# Remove shared markers
mnnCT7.raw.unique.id <- which(table(mnnCT7.raw.markers$gene) == 1)
mnnCT7.raw.specific.markers <- subset(mnnCT7.raw.markers, gene %in% names(mnnCT7.raw.unique.id))
table(mnnCT7.raw.specific.markers$cluster)
# write.csv(mnnCT7.raw.specific.markers, paste("./integration/mnnCT7.raw.specific.markers.pc", mnnCT7.raw.pc, ".d",
# mnnCT7.raw.dims, ".r", mnnCT7.raw.res, ".csv", sep = ""))
# Plot markers
mnnCT7.raw.top10 <- mnnCT7.raw.specific.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)
DoHeatmap(object = mnnCT7.raw, features = as.character(mnnCT7.raw.top10$gene)) + NoLegend()
#### Enrichment analysis ####
mnnCT7.raw.go.list <- split.data.frame(mnnCT7.raw@misc$specific.markers, mnnCT7.raw@misc$specific.markers$cluster) %>%
lapply("[[", "gene") %>%
lapply(gost)
# Extract GO results
mnnCT7.raw.result.list <-
lapply(mnnCT7.raw.go.list, function(go) {go$result %>% filter(term_size <1000) %>% dplyr::select(1:13)}) %>%
lapply(function(x) {x$cluster <- (parent.frame()$i[]-1); return(x)})
mnnCT7.raw.go <- do.call(rbind, mnnCT7.raw.result.list)
table(mnnCT7.raw.go$source, mnnCT7.raw.go$cluster)
#### Infercnv ####
# prepare annotation file and count matrix
Leuk.marker <- FindMarkers(mnnCT7.raw, group.by = "cluster", ident.1 = "Leuk", min.pct = 0.1, logfc.threshold = 0.1, only.pos = T)
Leuk.gene <- Leuk.marker %>% tibble::rownames_to_column("gene") %>% filter(p_val_adj < 0.05) %>% pull(gene)
length(Leuk.gene)
write.table(mnnCT7.raw[setdiff(rownames(mnnCT7.raw), Leuk.gene),]@meta.data["cluster"], "./infercnv/mnnCT7.raw_Leuk_anno.txt",
quote = F, row.names = T, col.names = F, sep = "\t")
write.table(GetAssayData(mnnCT7.raw[setdiff(rownames(mnnCT7.raw), Leuk.gene),], "counts"), './infercnv/mnnCT7.raw_Leuk_counts.matrix', quote = F, sep = "\t")
# read denoised observation and reference file
mnnCT7.raw_refe <- read.table("./infercnv/mnnCT7.rawcnv/infercnv.21_denoised.references.txt")
mnnCT7.raw_obse <- read.table("./infercnv/mnnCT7.rawcnv/infercnv.21_denoised.observations.txt")
# Calculate and save total cnv level
mnnCT7.raw_cnv <- cbind(mnnCT7.raw_obse, mnnCT7.raw_refe)
mnnCT7.raw_cnv <- mnnCT7.raw_cnv[rownames(mnnCT7.raw_cnv) %in% rownames(infercnv::genes), ]
mnnCT7.raw_cnv <- colSums((mnnCT7.raw_cnv-1)^2, na.rm = T)
write.table(mnnCT7.raw_cnv, "./infercnv/mnnCT7.raw_cnv.txt", quote = F, col.names = F)
# Read cnv_level
mnnCT7.raw_cnv <- read.delim("./infercnv/mnnCT7.raw_cnv.txt", sep = " ", header = F)
mnnCT7.raw_cnv <- setNames(mnnCT7.raw_cnv$V2, mnnCT7.raw_cnv$V1)
# Add cnv to meta data
[email protected]$cnv <- mnnCT7.raw_cnv[colnames(mnnCT7.raw)]
VlnPlot(mnnCT7.raw, "cnv", pt.size = 0, split.by = "patient", group.by = "cluster", cols = col.patient)
#### AddModuleScore ####
# Marker gene score
gene.list <- split.data.frame(mnnCT7.raw.specific.markers, mnnCT7.raw.specific.markers$cluster) %>%
lapply("[[", "gene")
mnnCT7.raw <- AddModuleScore(mnnCT7.raw, gene.list, assay = "RNA")
cluster.score <- paste0("cluster", levels(mnnCT7.raw), ".score")
colnames([email protected])[grep("Cluster", colnames([email protected]))] <- cluster.score
cluster.score.plot.list <- mapply(function(feature, ident, col){
(VlnPlot(mnnCT7.raw, features = feature, idents = ident, group.by = "orig.ident", pt.size = 0.1, cols = col) +
NoLegend()) %>% list()
}, cluster.score, levels(mnnCT7.raw), list(col.patient))
CombinePlots(cluster.score.plot.list, ncol = 4)
# Mean score matrix
score.cut.off <- 0.35
mean.score.matrix <- data.frame(patient = object.name)
for (i in 0:(length(levels(mnnCT7.raw))-1)) {
scroe <- paste0("cluster", i, ".score")
mean.score <- [email protected] %>%
# tibble::rownames_to_column("cell.id") %>%
group_by(patient) %>%
filter(seurat_clusters == i) %>%
summarise(mean(!!ensym(scroe)))
colnames(mean.score)[2] <- i
mean.score.matrix <- right_join(mean.score, mean.score.matrix)
}
mean.score.matrix
# Average cluster score
mean.score.melt <- reshape::melt(mean.score.matrix %>% as.data.frame())
colnames(mean.score.melt)[3] <- "mean"
mean.score.melt$mean.cut.off <- mean.score.melt$mean > score.cut.off
# Distance to highest scroe
mean.score.dist.matrix <- t(rowMaxs(t(mean.score.matrix[-1]), na.rm = T) - t(mean.score.matrix[-1])) %>%
cbind(mean.score.matrix[1])
mean.score.dist.melt <- reshape::melt(mean.score.dist.matrix %>% as.data.frame())
colnames(mean.score.dist.melt)[3] <- "dist"
mean.score.dist.melt$dist.cut.off <- mean.score.dist.melt$dist < score.cut.off
mean.score.melt <- left_join(mean.score.melt, mean.score.dist.melt)
mean.score.melt$denosed <- mean.score.melt$mean.cut.off & mean.score.melt$dist.cut.off
mean.score.melt$mark <- ifelse(mean.score.melt$denosed == T, "*", "")
mean.score.melt$patient <- factor(mean.score.melt$patient, levels = object.name)
mean.score.melt$variable <- factor(mean.score.melt$variable, levels = levels(mnnCT7.raw))
mean.score.melt$cluster <- CT7.cluster[mean.score.melt$variable]
mean.score.melt$cluster <- factor(mean.score.melt$cluster, levels = CT7.cluster[c(0,5,4,1,3,7,2,6)+1])
# mnnCT7.raw@misc$mean.score.melt <- mean.score.melt
ggplot(mean.score.melt, aes(x = cluster, y = mean, fill = patient)) +
geom_bar(stat = "identity", position = "dodge") +
geom_hline(yintercept = score.cut.off, col = "red") +
theme_classic() +
xlab("cluster") + ylab("average cluster score") + labs(fill = "patient")+
geom_text(aes(label = mark), position=position_dodge(width=0.9), vjust=-0.25)+
scale_fill_manual(values = col.patient)
ggplot(mean.score.melt, aes(x = variable, y = dist, fill = patient)) +
geom_bar(stat = "identity", position = "dodge") +
geom_hline(yintercept = score.cut.off, col = "red") +
theme_classic() +
xlab("cluster") + ylab("distance to highest scroe") + labs(fill = "patient") +
geom_text(aes(label = mark), position=position_dodge(width=0.9), vjust=-0.25)+
scale_fill_manual(values = col.patient)
#### Denoising ####
# Include all patient of cluster 3
mean.score.melt$denosed.2 <- mean.score.melt$denosed
mean.score.melt[which(mean.score.melt$variable == 2), "denosed.2"] <- TRUE
mean.score.denosed <- subset(mean.score.melt, denosed.2 == T) %>% mutate(test = paste(patient, variable, sep = "-"))
cell.denosed <- paste(mnnCT7.raw$orig.ident, mnnCT7.raw$seurat_clusters, sep = "-") %in% mean.score.denosed$test
table(cell.denosed)
# Sebset mnnCHS7.raw with clear cells
mnnCT7 <- mnnCT7.raw[, cell.denosed]
# corrected cluster score plot
cluster.score.plot.list <- mapply(function(feature, ident, col){
(VlnPlot(mnnCT7, features = feature, idents = ident, group.by = "orig.ident", pt.size = 0.1, cols = col) +
NoLegend()) %>% list()
}, cluster.score, levels(mnnCT7), list(col.patient))
CombinePlots(cluster.score.plot.list, ncol = 4)
# Name cluster
mnnCT7$cluster.id <- mnnCT7$seurat_clusters
names(CT7.cluster) <- levels(mnnCT7)
mnnCT7$cluster <- factor(recode(mnnCT7$cluster.id, !!!CT7.cluster), levels = CT7.cluster[c(0,5,4,1,3,7,2,6)+1])
mnnCT7$subcluster <- factor(mnnCT7$subcluster, levels = c(c("High1a", "High1b", "High2a", "High2b", "nStro", "Stro1"),
CT7.cluster[c(0,5,4,1,3,7,2,6)+1]))
#########################
#### Denoised mnnCT7 ####
#########################
#### Finding differentially expressed features ####
# Finding differentially expressed features (cluster biomarkers)
DefaultAssay(mnnCT7) <- "RNA"
mnnCT7 <- ScaleData(mnnCT7, features = rownames(mnnCT7))
mnnCT7.markers <- FindAllMarkers(object = mnnCT7, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
mnnCT7.markers <- subset(mnnCT7.markers, p_val_adj < 0.05 & (pct.1 - pct.2) > 0)
table(mnnCT7.markers$cluster)
# Remove shared markers
mnnCT7.unique.id <- which(table(mnnCT7.markers$gene) == 1)
mnnCT7.specific.markers <- subset(mnnCT7.markers, gene %in% names(mnnCT7.unique.id))
table(mnnCT7.specific.markers$cluster)
# Plot markers
mnnCT7.top10 <- mnnCT7.specific.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)
DoHeatmap(object = mnnCT7, features = as.character(mnnCT7.top10$gene)) + NoLegend()
#### UMAP and Heatmap ####
[email protected]$orig.ident <- factor([email protected]$orig.ident, levels = patients)
DimPlot(mnnCT7, group.by = c("cluster", "patient"), cols = c(col.CT7, col.patient))
DimPlot(mnnCT7, split.by = c("orig.ident"), group.by = "ident", label = T, cols = col.CT7, ncol = 4) +
NoLegend()
ggplot([email protected], aes(x = patient, fill = cluster)) +
geom_bar(stat = "count", position = "fill") +
scale_fill_manual(values = col.CT7) +
xlab("patient") + ylab("percentage") + labs(fill = "cluster") +
theme_classic() +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
#### Enrichment analysis ####
mnnCT7.go.list <- split.data.frame(mnnCT7@misc$specific.markers, mnnCT7@misc$specific.markers$cluster) %>%
lapply("[[", "gene") %>%
lapply(gost)
# Extract GO results
mnnCT7.result.list <-
lapply(mnnCT7.go.list, function(go) {go$result %>% filter(term_size <1000) %>% dplyr::select(1:13)}) %>%
lapply(function(x) {x$cluster <- (parent.frame()$i[]-1); return(x)})
mnnCT7.go <- do.call(rbind, mnnCT7.result.list)
table(mnnCT7.go$source, mnnCT7.go$cluster)
#### Cell cycle analysis ####
exp.mat <- read.table(file = "./cell_cycle/nestorawa_forcellcycle_expressionMatrix.txt",
header = TRUE, as.is = TRUE, row.names = 1)
cc.genes <- readLines(con = "./cell_cycle/regev_lab_cell_cycle_genes.txt")
s.genes <- cc.genes[1:43]
g2m.genes <- cc.genes[44:97]
mnnCT7 <- CellCycleScoring(object = mnnCT7, s.features = s.genes, g2m.features = g2m.genes, set.ident = F)
levels(mnnCT7$Phase) <- c("G1", "S", "G2M")
DimPlot(mnnCT7, reduction = "umap", group.by = "Phase")
# Plot cell cycle and clusters
ggplot([email protected], aes(x = seurat_clusters, fill = Phase)) +
geom_bar(position = "fill") +
scale_y_continuous(labels = scales::percent) +
xlab("cluster") + ylab("percentage") +
# facet_grid(. ~ orig.ident) +
theme_classic() +
NoLegend()
ggplot([email protected], aes(x = orig.ident, fill = Phase)) +
geom_bar(position = "fill") +
scale_y_continuous(labels = scales::percent) +
xlab("patient") + ylab("percentage") +
# facet_grid(. ~ orig.ident) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
#### Infercnv filtering marker genes ####
# Finding differentially expressed features (cluster biomarkers)
DefaultAssay(mnnCT7) <- "RNA"
mnnCT7 <- ScaleData(mnnCT7, features = rownames(mnnCT7))
mnnCT7.markers <- FindAllMarkers(object = mnnCT7, only.pos = T, min.pct = 0.1, logfc.threshold = 0.1)
mnnCT7.markers <- subset(mnnCT7.markers, p_val_adj < 0.05 & (pct.1 - pct.2) > 0)
table(mnnCT7.markers$cluster)
# write.csv(mnnCT7.markers, "integration/mnnCT7.markers.min.pct.1.logfc.1.csv")
mnnCT7.markers <- read.csv("integration/mnnCT7.markers.min.pct.1.logfc.1.csv", header = T, row.names = 1, stringsAsFactors = F)
mnnCT7.markers$cluster.name <- CT7.cluster[mnnCT7.markers$cluster %>% as.character()]
length(unique(mnnCT7.markers$gene))
markers <- setdiff(rownames(mnnCT7), mnnCT7@misc$specific.markers$gene)
length(markers)
# markers <- setdiff(rownames(mnnCT7), mnnCT7.markers$gene[mnnCT7.markers$cluster.name == "Leuk"])
# length(markers)
write.table(mnnCT7[markers,]@meta.data["cluster"], "./infercnv/mnnCT7_specific.marker_anno.txt",
quote = F, row.names = T, col.names = F, sep = "\t")
write.table(GetAssayData(mnnCT7[markers,], "counts"), './infercnv/mnnCT7_specific.marker_counts.matrix', quote = F, sep = "\t")
#### Infercnv line plot ####
# read denoised observation and reference file
mnnCT7_refe <- read.table("./infercnv/mnnCT7_Leuk_cnv/infercnv.21_denoised.references.txt")
mnnCT7_obse <- read.table("./infercnv/mnnCT7_Leuk_cnv/infercnv.21_denoised.observations.txt")
cnv <- cbind(mnnCT7_obse, mnnCT7_refe)
cnv.genes <- read.table("infercnv/gene.coor.csv", header = T, row.names = 1, sep = ",", stringsAsFactors = F)
rownames(cnv.genes) <- cnv.genes$gene_name
# Subset cnv
gene <- cnv.genes[rownames(cnv.genes) %in% rownames(cnv), ]
mnnCT7.cnv <- cnv[rownames(gene), colnames(mnnCT7)] %>% t()
mnnCT7.cnv[1:5, 1:5]
dim(mnnCT7.cnv)
# Subset cells according to patients and clusters (cluster with more than 20 cells)
cell.list <- split.data.frame([email protected], mnnCT7$patient) %>%
lapply(function(x) split(x$cell.id, x$cnvcluster))
cell.list <- lapply(cell.list, function(x) x[sapply(x, length) > 20])
# Plot chondoid tumors
# Add gaps_col
gaps_col <- gene %>% group_by(gene[, "rank"]) %>% tibble::rowid_to_column("rownames") %>%
top_n(1, rownames) %>% pull(rownames)
gaps_col[c(5,10,18,20)] <- gaps_col[c(5,10,18,20)] + 1
gaps_col[1:10] <- gaps_col[1:10] - 1
gene$chr <- ""
gene$chr[gaps_col - table(gene$rank)%/%2] <- 1:22
# Add highlighted genes
gene$col <- gene$rank
# gene$col[which(gene$gene_name == "COL10A1"):(which(gene$gene_name == "COL10A1") + 10)] <- "anno"
#
# gene$chr[which(gene$gene_name == "COL10A1")] <- "COL10A1"
# Mean CNV level of each regin within cell list
patient.cnv <- lapply(cell.list[1:4], function(patient) {
sapply(patient, function(x) rowMeans(cnv[,x, drop = F]))
})
# chon.cnv <- patient.cnv[1:4]
# Create plot list
cnv.list <- lapply(patient.cnv, function(x) {
# Melt the data for ploting
join.mtx <- cbind(gene, x[gene$gene_name,])
join.melt <- reshape::melt(join.mtx, id.vars = colnames(gene))
join.melt$gene_name <- factor(join.melt$gene_name, levels = gene$gene_name)
# Plot data
ggplot(join.melt) +
geom_line(aes(x = gene_name, y = value, colour = variable, group = variable)) +
scale_color_manual(values = col.allcnv) +
xlab("chromosome") + ylab(names(patient.cnv)[parent.frame()$i[]]) +
theme(axis.ticks.x = element_line(colour = col.chr[join.melt$col]), legend.title = element_blank(),
axis.text.x = element_text(size = 8), axis.text.y = element_text(size = 7),
axis.title = element_text(size = 12), axis.line.y = element_line()) +
scale_x_discrete(labels= join.melt$chr, guide = guide_axis(n.dodge=2)) +
geom_hline(yintercept = c(0.9, 1.1), col = "gray", linetype = "dashed") +
ylim(min(mnnCT7.cnv), max(mnnCT7.cnv)) +
NoLegend()
})
# Add annotation box for ch6
cnv.list[2:4] <- lapply(cnv.list[2:4], function(x) x +
geom_rect(aes(xmin= "SENP6", xmax= "PDCD2",
ymin= min(mnnCT7.cnv), ymax= max(mnnCT7.cnv)),
color = "gray50", alpha = 0, linetype = "longdash")
)
# Add annotation box for ch11 and Ch19 of Low_L28
gene.11 <- range(which(gene$rank == 11))
cnv.list[3] <- lapply(cnv.list[3], function(x) x +
geom_rect(aes(xmin= gene.11[1], xmax= gene.11[2],
ymin= min(mnnCT7.cnv), ymax= max(mnnCT7.cnv)),
color = "blue", alpha = 0, linetype = "longdash")
)
gene.19 <- range(which(gene$rank == 19))
cnv.list[3] <- lapply(cnv.list[3], function(x) x +
geom_rect(aes(xmin= gene.19[1], xmax= gene.19[2],
ymin= min(mnnCT7.cnv), ymax= max(mnnCT7.cnv)),
color = "red", alpha = 0, linetype = "longdash")
)
# Remove x axis annotation
cnv.list[1:3] <- lapply(cnv.list[1:3], function(x) {
x + theme(axis.text.x = element_blank(), axis.title.x = element_blank())
})
# Merge Chon plots
cnv.plot.null <- plot_grid(plotlist = cnv.list[1:4], ncol = 1, rel_heights = c(1,1,1,1.6), greedy = F)
cnv.plot.ylab <- textGrob("mean CNV acrosss cluster", rot = 90)
cnv.plot.L <- (ggplot([email protected][mnnCT7$histology %in% c("Ben", "Low", "Med"),]) +
geom_line(aes(x= patient, y = 1, group = subcluster, color = subcluster)) + theme_classic() +
scale_color_manual(values = col.all, breaks = c("Chon1", "Chon2", "Stro", "Leuk")) +
labs(col = "cluster")) %>% get_legend()
cnv.plot <- plot_grid(cnv.plot.ylab, cnv.plot.null, cnv.plot.L, nrow = 1, rel_widths = c(2,50,7))
# saveRDS(cnv.plot, "figure/chon.cnv.plot_thesis.rds")
# Save CNV gene at chromosome 6
# write.csv(cnv.genes[which(cnv.genes$gene_name == "SENP6"):which(cnv.genes$gene_name == "PDCD2"), ],
# "infercnv/cnv_gene/cnv_gene_ch6.csv")
# Plot high grade tumors
# Add gaps_col
gaps_col <- gene %>% group_by(gene[, "rank"]) %>% tibble::rowid_to_column("rownames") %>%
top_n(1, rownames) %>% pull(rownames)
gaps_col[c(5,10,18,20)] <- gaps_col[c(5,10,18,20)] + 1
gene$chr <- ""
gene$chr[gaps_col - table(gene$rank)%/%2] <- 1:22
# Add highlighted genes
gene$col <- gene$rank
gene$col[which(gene$gene_name == "TGFB2"):(which(gene$gene_name == "TGFB2") + 4)] <- "anno"
gene$chr[which(gene$gene_name == "TGFB2") + 1] <- "TGFB2"
# Mean CNV level of each regin within cell list
patient.cnv <- lapply(cell.list[5:7], function(patient) {
sapply(patient, function(x) rowMeans(cnv[,x, drop = F]))
})
# Create plot list
cnv.list <- lapply(patient.cnv, function(x) {
# Melt the data for ploting
join.mtx <- cbind(gene, x[gene$gene_name,])
join.melt <- reshape::melt(join.mtx, id.vars = colnames(gene))
join.melt$gene_name <- factor(join.melt$gene_name, levels = gene$gene_name)
# Plot data
ggplot(join.melt) +
geom_line(aes(x = gene_name, y = value, colour = variable, group = variable)) +
scale_color_manual(values = col.allcnv) +
xlab("chromosome") + ylab(names(patient.cnv)[parent.frame()$i[]]) +
theme(axis.ticks.x = element_line(colour = col.chr[join.melt$col]), legend.title = element_blank(),
axis.text.x = element_text(size = 8), axis.text.y = element_text(size = 7),
axis.title = element_text(size = 10), axis.line.y = element_line()) +
scale_x_discrete(labels= join.melt$chr, guide = guide_axis(n.dodge=2)) +
geom_hline(yintercept = c(0.9, 1.1), col = "gray", linetype = "dashed") +
ylim(min(mnnCT7.cnv), max(mnnCT7.cnv)) +
NoLegend()
})
# Add annotation box for L31
cnv.list[[1]] <- cnv.list[[1]] +
geom_rect(aes(xmin= "NBPF15", xmax= "ZNF672",
ymin= min(mnnCT7.cnv), ymax= max(mnnCT7.cnv)),
color = "dodgerblue", alpha = 0)
# Remove x axis annotation
cnv.list[1:2] <- lapply(cnv.list[1:2], function(x) {
x + theme(axis.text.x = element_blank(), axis.title.x = element_blank())
})
# Merge Chon plots
cnv.plot.null <- plot_grid(plotlist = cnv.list, ncol = 1, rel_heights = c(1,1,1.3), greedy = F)
cnv.plot.ylab <- textGrob("mean CNV acrosss cluster", rot = 90)
cnv.plot.L <- (ggplot([email protected]) +
geom_line(aes(x= patient, y = 1, group = cnvcluster, color = cnvcluster)) + theme_classic() +
scale_color_manual(values = col.allcnv, breaks = c("Undi", "Hype", "Myxo", "nStro", "Stro1", "Leuk")) +
labs(col = "cluster")) %>% get_legend()
cnv.plot <- plot_grid(cnv.plot.ylab, cnv.plot.null, cnv.plot.L, nrow = 1, rel_widths = c(2,50,5))
# saveRDS(cnv.plot, "figure/high.cnv.plot.rds")
#### Analysis on cnv of specific chromosomes of Low_L28 ####
# read denoised observation and reference file
mnnCT7_refe <- read.table("./infercnv/mnnCT7_Leuk_cnv/infercnv.21_denoised.references.txt")
mnnCT7_obse <- read.table("./infercnv/mnnCT7_Leuk_cnv/infercnv.21_denoised.observations.txt")
cnv <- cbind(mnnCT7_obse, mnnCT7_refe)
cnv[1:5, 1:5]
cnv.genes <- read.table("infercnv/gene.coor.csv", header = T, row.names = 1, sep = ",", stringsAsFactors = F)
rownames(cnv.genes) <- cnv.genes$gene_name
# Subset cnv
gene <- cnv.genes[rownames(cnv.genes) %in% rownames(cnv), ]
head(gene)
# Chromosome 19
cells <- [email protected] %>% filter(patient == "Low_L28" & cluster %in% c("Chon1", "Chon2")) %>% pull(cell.id)
L28.19 <- cnv[gene$chr == "19", cells]
dim(L28.19)
saveRDS(L28.19, "infercnv/heatmap/L28.19.rds")
ha <- HeatmapAnnotation(cluster = [email protected][cells, "cluster"],
which = "column",
col = list(cluster = col.all[c("Chon1", "Chon2")]))
L28.19.heatmap <- Heatmap(L28.19,
name = "CNV",
col = circlize::colorRamp2(c(1.2,1,0.8), c("red", "white", "blue")),
top_annotation = ha,
cluster_rows = F,
show_column_names = F,
show_row_names = F,
column_split = 4,
clustering_method_columns = "ward.D2")
L28.19.hc <- hclust(dist(t(L28.19)), method = "ward.D2")
L28.19.cluster <- cutree(L28.19.hc, k = 2)
table(L28.19.cluster)
mnnCT7$L28.19.cluster <- NA
[email protected][names(L28.19.cluster), "L28.19.cluster"] <- L28.19.cluster
DimPlot(mnnCT7[, cells], group.by = "L28.19.cluster")
L28.19.cnv <- L28.19 -1
table(L28.19.cnv<0)
L28.19.cnv[L28.19.cnv<0] <- 0
table(L28.19.cnv<0)
L28.19.cnv <- abs(colSums(L28.19.cnv))
mnnCT7$L28.19.cluster <- L28.19.cnv
[email protected][names(L28.19.cnv), "L28.19.cnv"] <- L28.19.cnv
FeaturePlot(mnnCT7[, cells], features = "L28.19.cnv", cols = c("whitesmoke", "red"), max.cutoff = 70, min.cutoff = 40)
# Chromosome 11
cells <- [email protected] %>% filter(patient == "Low_L28" & cluster %in% c("Chon1", "Chon2")) %>% pull(cell.id)
L28.11 <- cnv[gene$chr == "11", cells]
dim(L28.11)