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ovar_3BAE2L_RNA-4.R
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ovar_3BAE2L_RNA-4.R
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###########################################################
# Matt Regner
# Franco Lab
# Date: May-December 2020
#
# Description: This script performs the following tasks
# 1) scRNA-seq processing before doublet removal
# 2) Doublet detection and removal
# 3) scRNA-seq processing after doublet removal
# 4) SingleR cell typing
###########################################################
# Change to your working directory
dir <- "."
setwd(dir)
#Change to your R library path
########################################################################
.libPaths('/home/regnerm/anaconda3/envs/r-environment/lib/R/library')
source("./rowr.R")
########################################################################
library(scater)
library(dplyr)
library(Seurat)
library(patchwork)
library(SingleCellExperiment)
library(scater)
library(ComplexHeatmap)
library(ConsensusClusterPlus)
library(msigdbr)
library(fgsea)
library(dplyr)
library(tibble)
library(DoubletFinder)
library(Signac)
library(ggplot2)
library(stringr)
library(SingleR)
# Define filepaths/variables/signature sets:
###############################################
# PanglaoDB
tsv=gzfile("./PanglaoDB_markers_27_Mar_2020.tsv.gz")
panglaodb <- read.csv(tsv,header=T,sep = "\t")
panglaodb <- dplyr::filter(panglaodb,species == "Hs" | species == "Mm Hs")# Human subset
panglaodb <- dplyr::filter(panglaodb,organ == "Connective tissue" |
organ == "Epithelium" |
organ == "Immune system" |
organ == "Reproductive"|
organ == "Vasculature" |
organ == "Smooth muscle"
)
panglaodb <- split(as.character(panglaodb$official.gene.symbol), panglaodb$cell.type)
# ESTIMATE signatures
ESTIMATE.signatures <- "./ESTIMATE_signatures.csv"
GRCH38.annotations <- "./Homo_sapiens.GRCh38.86.txt"
doublet.rate = 0.0610
SAMPLE.ID = "ovar_3BAE2L"
###########################################################
# Part 1: scRNA-seq processing before doublet removal
###########################################################
# Load the RNA dataset
counts <- Read10X_h5(filename= "./filtered_feature_bc_matrix.h5")
# Initialize the Seurat object with the raw (non-normalized data).
rna <- CreateSeuratObject(counts = counts, min.cells = 3)# genes not present in at least 3 cells are removed
rna
PreQCNumCells <- length(colnames(rna))
# The [[ operator can add columns to object metadata. This is a great place to stash QC stats
rna[["percent.mt"]] <- PercentageFeatureSet(rna, pattern = "^MT-")
# QC metrics: nCount_RNA, nFeature_RNA, and percent mitochrondial counts
# Outliers are >2 MADs
[email protected]$nCount_RNA_outlier_2mad <- isOutlier(log([email protected]$nCount_RNA),log = F,type = "lower",nmads = 2)
[email protected]$nFeature_RNA_outlier_2mad <- isOutlier(log([email protected]$nFeature_RNA),log = F,type = "lower",nmads = 2)
[email protected]$percent_mt_outlier_2mad <- isOutlier(log1p([email protected]$percent.mt),log = F,type = "higher",nmads = 2)
rna <- subset(rna, subset = nCount_RNA_outlier_2mad == "FALSE" &
nFeature_RNA_outlier_2mad == 'FALSE' &
percent_mt_outlier_2mad == "FALSE")
PostQCNumCells <- length(colnames(rna))
# Default Seurat processing
rna <- NormalizeData(rna, normalization.method = "LogNormalize", scale.factor = 10000)
#Feature Selection
rna <- FindVariableFeatures(rna, selection.method = "vst", nfeatures = 2000)
#Scaling & PCA
all.genes <- rownames(rna)
rna <- ScaleData(rna, features = all.genes)
rna <- RunPCA(rna)
# Score cells for immune/stromal/fibroblast/endothelial signatures
############################################################################################
immune.stromal <- read.csv(ESTIMATE.signatures,header = F)
stromal <- immune.stromal$V1[1:141]
immune <- immune.stromal$V1[142:282]
fibroblast <- panglaodb$Fibroblasts
endothelial <- panglaodb$`Endothelial cells`
epithelial <- panglaodb$`Epithelial cells`
smooth <- panglaodb$`Smooth muscle cells`
plasma <- panglaodb$`Plasma cells`
feats <- list(stromal,immune,fibroblast,endothelial,epithelial,smooth,plasma)
rna <- AddModuleScore(rna,features = feats,name = c("stromal.","immune.","fibroblast.","endothelial.",
"epithelial.","smooth.","plasma."),search = T)
#########################################################################
#######################################################################
# Add PC1 to metadata
[email protected]$PC1 <- rna@[email protected][,1]
count_cor_PC1 <- cor(rna$PC1,rna$nCount_RNA,method = "spearman")
stromal.cor <- cor(rna$PC1,rna$stromal.1,method = "spearman")
immune.cor <- cor(rna$PC1,rna$immune.2,method = "spearman")
fibroblast.cor <- cor(rna$PC1,rna$fibroblast.3,method = "spearman")
endothelial.cor <- cor(rna$PC1,rna$endothelial.4,method = "spearman")
epithelial.cor <- cor(rna$PC1,rna$epithelial.5,method = "spearman")
smooth.cor <- cor(rna$PC1,rna$smooth.6,method = "spearman")
plasma.cor <- cor(rna$PC1,rna$plasma.7,method = "spearman")
# Make JackStraw Plot
rna <- JackStraw(rna, num.replicate = 100,dims = 50)
rna <- ScoreJackStraw(rna,dims = 1:50)
JackStrawPlot(rna, dims = 1:50)+ggsave("JackStraw_predoublet.png")
# If PC1 is correalted with read depth, check to see if biological variation is corralted to PC1
if (round(abs(count_cor_PC1),2) > 0.5){
if( round(abs(stromal.cor),2) >= 0.5 |
round(abs(immune.cor),2) >= 0.5 |
round(abs(fibroblast.cor),2) >= 0.5 |
round(abs(endothelial.cor),2) >= 0.5 |
round(abs(epithelial.cor),2) >= 0.5 |
round(abs(smooth.cor),2) >= 0.5 |
round(abs(plasma.cor),2) >= 0.5){
rna <- FindNeighbors(rna,dims = 1:50)
rna <- FindClusters(rna,resolution = 0.7)
rna <- RunUMAP(rna,dims = 1:50)
Idents(rna) <- "RNA_snn_res.0.7"
# Verify with inferCNV: is PC1 correlated with CNV events/Malignancy?
#########################################################################
# inferCNV: does PC1 also correlated with CNV/malignancy status?
library(infercnv)
library(stringr)
library(Seurat)
counts_matrix = GetAssayData(rna, slot="counts")
# Identify immune clusters
#######################################################
# Find immune cells by relative enrichment of ESTIMATE immune signature
library(psych)
test <- VlnPlot(rna,features = "immune.2")
data <- describeBy(test$data$immune.2, test$data$ident, mat = TRUE)
data.immune <- dplyr::filter(data,median > 0.1)
test <- VlnPlot(rna,features = "plasma.7")
data <- describeBy(test$data$plasma.7, test$data$ident, mat = TRUE)
data.plasma <- dplyr::filter(data,median > 0.1)
immune.clusters <- intersect(data.immune$group1,levels(Idents(rna)))
plasma.clusters <- intersect(data.plasma$group1,levels(Idents(rna)))
immune.clusters <- unique(append(immune.clusters,plasma.clusters))
for (i in 1:length(immune.clusters)){
j <- which(levels(Idents(rna)) == immune.clusters[i])
levels(Idents(rna))[j] <- paste0("immune.",immune.clusters[i])
}
[email protected]$predoublet.idents <- Idents(rna)
idents <- data.frame(rownames([email protected]),[email protected]$predoublet.idents)
colnames(idents) <- c("V1","V2")
saveRDS(rna,"./rna_predoublet_preinferCNV.rds")
# Make inferCNV input files
rownames(idents) <- NULL
colnames(idents) <- NULL
write.table(idents,"./sample_annotation_file_inferCNV.txt",sep = "\t",row.names = FALSE)
idents <- read.delim("./sample_annotation_file_inferCNV.txt",header = F)
gtf <- read.delim(GRCH38.annotations,header = F)
library(EnsDb.Hsapiens.v86)
convert.symbol = function(Data){
ensembls <- Data$V1
ensembls <- gsub("\\.[0-9]*$", "", ensembls)
geneIDs1 <- ensembldb::select(EnsDb.Hsapiens.v86, keys= ensembls, keytype = "GENEID", columns = "SYMBOL")
Data <- cbind.fill(Data, geneIDs1, fill = NA)
Data <- na.omit(Data)
Data$feature <- Data$SYMBOL
Data.new <- data.frame(Data$SYMBOL,Data$V2,Data$V3,Data$V4)
Data.new$Data.V2 <- paste("chr",Data.new$Data.V2,sep = "")
Data.new$Data.SYMBOL <- make.unique(Data.new$Data.SYMBOL)
return(Data.new)
}
gtf <- convert.symbol(gtf)
head(gtf)
write.table(gtf,"./Homo_sapiens.GRCh38.86.symbol.txt",sep = "\t",row.names = FALSE,col.names = FALSE)
num.immune.clusters = length(immune.clusters)
# create the infercnv object
if ( num.immune.clusters == 1) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=paste0("immune.",immune.clusters[1]))
} else if (num.immune.clusters == 2) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2])))
} else if ( num.immune.clusters == 3 ) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3])))
} else if (num.immune.clusters == 4) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3]),
paste0("immune.",immune.clusters[4])))
} else if (num.immune.clusters == 5) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3]),
paste0("immune.",immune.clusters[4]),
paste0("immune.",immune.clusters[5])))
} else if (num.immune.clusters == 6) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3]),
paste0("immune.",immune.clusters[4]),
paste0("immune.",immune.clusters[5]),
paste0("immune.",immune.clusters[6])))
}else if (num.immune.clusters == 7) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3]),
paste0("immune.",immune.clusters[4]),
paste0("immune.",immune.clusters[5]),
paste0("immune.",immune.clusters[6]),
paste0("immune.",immune.clusters[7])))
}else if (num.immune.clusters == 8) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3]),
paste0("immune.",immune.clusters[4]),
paste0("immune.",immune.clusters[5]),
paste0("immune.",immune.clusters[6]),
paste0("immune.",immune.clusters[7]),
paste0("immune.",immune.clusters[8])))
}else if (num.immune.clusters == 9) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3]),
paste0("immune.",immune.clusters[4]),
paste0("immune.",immune.clusters[5]),
paste0("immune.",immune.clusters[6]),
paste0("immune.",immune.clusters[7]),
paste0("immune.",immune.clusters[8]),
paste0("immune.",immune.clusters[9])))
}else if (num.immune.clusters == 10) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3]),
paste0("immune.",immune.clusters[4]),
paste0("immune.",immune.clusters[5]),
paste0("immune.",immune.clusters[6]),
paste0("immune.",immune.clusters[7]),
paste0("immune.",immune.clusters[8]),
paste0("immune.",immune.clusters[9]),
paste0("immune.",immune.clusters[10])))
}
# perform infercnv operations to reveal cnv signal
infercnv_obj = infercnv::run(infercnv_obj,
cutoff=0.1, # use 1 for smart-seq, 0.1 for 10x-genomics
out_dir="./output_dir_CNV_predoublet", # dir is auto-created for storing outputs
cluster_by_groups=T, # cluster
denoise=T,scale_data = T,
HMM=T,HMM_type = "i6",analysis_mode = "samples",min_cells_per_gene = 10,
BayesMaxPNormal = 0.4, num_threads = 8
)
regions <- read.delim("./output_dir_CNV_predoublet/HMM_CNV_predictions.HMMi6.hmm_mode-samples.Pnorm_0.4.pred_cnv_regions.dat")
probs <- read.delim("./output_dir_CNV_predoublet/BayesNetOutput.HMMi6.hmm_mode-samples/CNV_State_Probabilities.dat")
probs <- as.data.frame(t(probs[3,]))
colnames(probs) <- "Prob.Normal"
probs <- dplyr::filter(probs,Prob.Normal < 0.05)
cnvs <- rownames(probs)
cnvs <- gsub("\\.","-",cnvs)
regions <- regions[regions$cnv_name %in% cnvs, ]
cnv.groups <- sub("\\..*", "", regions$cell_group_name)
length(which(rownames(rna@[email protected]) == rownames([email protected])))
rna$PC1.loading <- rna@[email protected][,1]
rna$cell.barcode <- rownames([email protected])
rna$CNV.Pos <- ifelse(as.character(rna$predoublet.idents) %in% cnv.groups,1,0)
cnv.freq <- data.frame(table(regions$cell_group_name))
cnv.freq$Var1 <- sub("\\..*", "", cnv.freq$Var1)
rna$Total_CNVs <- ifelse(as.character(rna$predoublet.idents) %in% cnv.freq$Var1,cnv.freq$Freq,0)
boxplot.cnv <- ggplot([email protected],aes(x= predoublet.idents,y=PC1.loading,color = as.factor(CNV.Pos)))+geom_boxplot()
boxplot.cnv+ggsave("Predoublet_CNV_PC1_boxplot.png")
data <- describeBy(boxplot.cnv$data$PC1.loading, boxplot.cnv$data$predoublet.idents, mat = TRUE)
data$CNV <- ifelse(data$group1 %in% cnv.groups,1,0)
wilcox <- wilcox.test(data = [email protected],PC1.loading~CNV.Pos)
if (wilcox$p.value < 0.05){
rna <- rna
library(stringr)
levels(Idents(rna)) <- str_remove(levels(Idents(rna)),"immune.")
saveRDS(rna,"./rna_predoublet_PassedPC1Checks.rds")
}else{
all.genes <- rownames(rna)
rna <- ScaleData(rna, features = all.genes,vars.to.regress = "nCount_RNA")
rna <- FindNeighbors(rna,dims = 1:50)
rna <- FindClusters(rna,resolution = 0.7)
rna <- RunUMAP(rna,dims = 1:50)
Idents(rna) <- "RNA_snn_res.0.7"
saveRDS(rna,"./rna_predoublet_FailedCNVTest.rds")
}
}else{
all.genes <- rownames(rna)
rna <- ScaleData(rna, features = all.genes,vars.to.regress = "nCount_RNA")
rna <- FindNeighbors(rna,dims = 1:50)
rna <- FindClusters(rna,resolution = 0.7)
rna <- RunUMAP(rna,dims = 1:50)
Idents(rna) <- "RNA_snn_res.0.7"
saveRDS(rna,"./rna_predoublet_FailedCorTest.rds")
}
}else{
rna <- FindNeighbors(rna,dims = 1:50)
rna <- FindClusters(rna,resolution = 0.7)
rna <- RunUMAP(rna,dims = 1:50)
Idents(rna) <- "RNA_snn_res.0.7"
saveRDS(rna,"./rna_predoublet_SkipChecks.rds")
}
###########################################################
# Part 2: Doublet detection and removal
###########################################################
#' # Doublet detection: 1) DoubletDecon 2) DoubletFinder 3) Take intersection of calls
######################################################################################
# # Doublet Decon
# # Add if else statement to regress out nCount RNA if needed before running DoubletDecon
library(DoubletDecon)
idents.length <- length(levels(Idents(rna)))
for (i in levels(Idents(rna))){
i <- as.numeric(i)
levels(Idents(rna))[i] <- i -1
}
#Improved_Seurat_Pre_Process()
#Idents(rna) <- as.factor(Idents(rna))
seuratObject=rna
newFiles=Improved_Seurat_Pre_Process(seuratObject, num_genes=50, write_files=F)
#dev.off()
results=Main_Doublet_Decon(rawDataFile=newFiles$newExpressionFile,
groupsFile=newFiles$newGroupsFile,
filename=SAMPLE.ID,
location="./DoubletDecon",
fullDataFile=NULL,
removeCC=FALSE,
species="hsa",
rhop=1.1,
write=F,
PMF=TRUE,
useFull=FALSE,
heatmap=FALSE,
centroids=TRUE,
num_doubs=100,
only50=FALSE,
min_uniq=4,
nCores=4)
decon.doublets <- rownames(results$Final_doublets_groups)
decon.doublets <- gsub("\\.","-",decon.doublets)
# DoubletFinder
# Add modfieid Parameter sweep function to regress out nCOunt RNA if needed
# Add if else statement to regress out nCount RNA if needed before running DoubletFinder
#########################################################################################
library(DoubletFinder)
# Estimate Doublets
#################################################################################################
## pK Identification (no ground-truth) ---------------------------------------------------------------------------------------
sweep.res.list <- paramSweep_v3(rna, PCs = 1:50, sct = FALSE)
sweep.stats <- summarizeSweep(sweep.res.list , GT = FALSE)
bcmvn<- find.pK(sweep.stats)
pK.1 <- as.numeric(unfactor(dplyr::arrange(bcmvn,desc(BCmetric))$pK[1]))
nExp_poi <- round(doublet.rate*length(colnames(counts))) ## Assuming 4.6% doublet formation rate - tailor for your dataset
# Run doubletFinder with pk.1
rna <- doubletFinder_v3(rna, PCs = 1:50, pN = 0.25, pK = pK.1, nExp = nExp_poi, reuse.pANN = FALSE, sct = FALSE)
meta <- [email protected]
doublet.column <- paste0("DF.classifications_0.25_",pK.1,"_",nExp_poi)
doublet.calls <- rna[[doublet.column]]
colnames(doublet.calls) <- "Call"
rna.dub <- dplyr::filter(doublet.calls, Call == "Doublet")
rna.singlet <- dplyr::filter(doublet.calls, Call == "Singlet")
DF.doublets <- rownames(rna.dub)
# Intersect doublet calls
###########################################################################
head(DF.doublets)
head(decon.doublets)
doublets <- intersect(decon.doublets,DF.doublets)
[email protected]$Doublet.Call <- ifelse(rownames([email protected]) %in% doublets,"TRUE","FALSE")
FeatureScatter(rna,feature1 = "nCount_RNA",feature2 = "nFeature_RNA",group.by = "Doublet.Call")+ggsave("Doublet_calls_FeatureScatter.png")
DimPlot(rna,group.by = "Doublet.Call")+ggsave("Doublet_calls_UMAP.png")
saveRDS(doublets,"Intersection_DoubletDecon_DoubletFinder_doublets.rds")
cells <- colnames(rna)
##################################################################################################################
###########################################################
# Part 3: scRNA-seq processing after doublet removal
###########################################################
setwd(dir)
# Load the RNA dataset
counts.init <- Read10X_h5(filename= "./filtered_feature_bc_matrix.h5")
# Initialize the Seurat object with the raw (non-normalized data).
rna <- CreateSeuratObject(counts = counts.init, min.cells = 3)# genes not present in at least 3 cells are removed
rna
rna <- rna[,cells]
rna <- rna[,!(colnames(rna) %in% doublets)]
dim(rna)
#Normalize
rna <- NormalizeData(rna, normalization.method = "LogNormalize", scale.factor = 10000)
#Feature Selection
rna <- FindVariableFeatures(rna, selection.method = "vst", nfeatures = 2000)
#Scaling
all.genes <- rownames(rna)
rna <- ScaleData(rna, features = all.genes)
rna <- RunPCA(rna)
feats <- list(stromal,immune,fibroblast,endothelial,epithelial,smooth,plasma)
rna <- AddModuleScore(rna,features = feats,name = c("stromal.","immune.","fibroblast.","endothelial.",
"epithelial.","smooth.","plasma."),search = T)
# Add PC1 to metadata
[email protected]$PC1 <- rna@[email protected][,1]
count_cor_PC1 <- cor(rna$PC1,rna$nCount_RNA,method = "spearman")
stromal.cor <- cor(rna$PC1,rna$stromal.1,method = "spearman")
immune.cor <- cor(rna$PC1,rna$immune.2,method = "spearman")
fibroblast.cor <- cor(rna$PC1,rna$fibroblast.3,method = "spearman")
endothelial.cor <- cor(rna$PC1,rna$endothelial.4,method = "spearman")
epithelial.cor <- cor(rna$PC1,rna$epithelial.5,method = "spearman")
smooth.cor <- cor(rna$PC1,rna$smooth.6,method = "spearman")
plasma.cor <- cor(rna$PC1,rna$plasma.7,method = "spearman")
# Make JackStraw Plot
rna <- JackStraw(rna, num.replicate = 100,dims = 50)
rna <- ScoreJackStraw(rna,dims = 1:50)
JackStrawPlot(rna, dims = 1:50)+ggsave("JackStraw_postdoublet.png")
# If PC1 is correalted with read depth, check to see if biological variation is corralted to PC1
if (round(abs(count_cor_PC1),2) > 0.5){
if( round(abs(stromal.cor),2) >= 0.5 |
round(abs(immune.cor),2) >= 0.5 |
round(abs(fibroblast.cor),2) >= 0.5 |
round(abs(endothelial.cor),2) >= 0.5 |
round(abs(epithelial.cor),2) >= 0.5 |
round(abs(smooth.cor),2) >= 0.5 |
round(abs(plasma.cor),2) >= 0.5){
rna <- FindNeighbors(rna,dims = 1:50)
rna <- FindClusters(rna,resolution = 0.7)
rna <- RunUMAP(rna,dims = 1:50)
Idents(rna) <- "RNA_snn_res.0.7"
# Verify with inferCNV: is PC1 correlated with CNV events/Malignancy?
#########################################################################
# inferCNV: does PC1 also correlated with CNV/malignancy status?
library(infercnv)
library(stringr)
library(Seurat)
counts_matrix = GetAssayData(rna, slot="counts")
# Identify immune clusters
#######################################################
# Find immune cells by relative enrichment of ESTIMATE immune signature
library(psych)
test <- VlnPlot(rna,features = "immune.2")
data <- describeBy(test$data$immune.2, test$data$ident, mat = TRUE)
data.immune <- dplyr::filter(data,median > 0.1)
test <- VlnPlot(rna,features = "plasma.7")
data <- describeBy(test$data$plasma.7, test$data$ident, mat = TRUE)
data.plasma <- dplyr::filter(data,median > 0.1)
immune.clusters <- intersect(data.immune$group1,levels(Idents(rna)))
plasma.clusters <- intersect(data.plasma$group1,levels(Idents(rna)))
immune.clusters <- unique(append(immune.clusters,plasma.clusters))
for (i in 1:length(immune.clusters)){
j <- which(levels(Idents(rna)) == immune.clusters[i])
levels(Idents(rna))[j] <- paste0("immune.",immune.clusters[i])
}
[email protected]$postdoublet.idents <- Idents(rna)
idents <- data.frame(rownames([email protected]),[email protected]$postdoublet.idents)
colnames(idents) <- c("V1","V2")
saveRDS(rna,"./rna_postdoublet_preinferCNV.rds")
# Make inferCNV inputs
rownames(idents) <- NULL
colnames(idents) <- NULL
write.table(idents,"./sample_annotation_file_inferCNV.txt",sep = "\t",row.names = FALSE)
idents <- read.delim("./sample_annotation_file_inferCNV.txt",header = F)
gtf <- read.delim(GRCH38.annotations,header = F)
library(EnsDb.Hsapiens.v86)
convert.symbol = function(Data){
ensembls <- Data$V1
ensembls <- gsub("\\.[0-9]*$", "", ensembls)
geneIDs1 <- ensembldb::select(EnsDb.Hsapiens.v86, keys= ensembls, keytype = "GENEID", columns = "SYMBOL")
Data <- cbind.fill(Data, geneIDs1, fill = NA)
Data <- na.omit(Data)
Data$feature <- Data$SYMBOL
Data.new <- data.frame(Data$SYMBOL,Data$V2,Data$V3,Data$V4)
Data.new$Data.V2 <- paste("chr",Data.new$Data.V2,sep = "")
Data.new$Data.SYMBOL <- make.unique(Data.new$Data.SYMBOL)
return(Data.new)
}
gtf <- convert.symbol(gtf)
head(gtf)
write.table(gtf,"./Homo_sapiens.GRCh38.86.symbol.txt",sep = "\t",row.names = FALSE,col.names = FALSE)
num.immune.clusters = length(immune.clusters)
# create the infercnv object
if ( num.immune.clusters == 1) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=paste0("immune.",immune.clusters[1]))
} else if (num.immune.clusters == 2) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2])))
} else if ( num.immune.clusters == 3 ) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3])))
} else if (num.immune.clusters == 4) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3]),
paste0("immune.",immune.clusters[4])))
} else if (num.immune.clusters == 5) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3]),
paste0("immune.",immune.clusters[4]),
paste0("immune.",immune.clusters[5])))
} else if (num.immune.clusters == 6) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3]),
paste0("immune.",immune.clusters[4]),
paste0("immune.",immune.clusters[5]),
paste0("immune.",immune.clusters[6])))
}else if (num.immune.clusters == 7) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3]),
paste0("immune.",immune.clusters[4]),
paste0("immune.",immune.clusters[5]),
paste0("immune.",immune.clusters[6]),
paste0("immune.",immune.clusters[7])))
}else if (num.immune.clusters == 8) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3]),
paste0("immune.",immune.clusters[4]),
paste0("immune.",immune.clusters[5]),
paste0("immune.",immune.clusters[6]),
paste0("immune.",immune.clusters[7]),
paste0("immune.",immune.clusters[8])))
}else if (num.immune.clusters == 9) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3]),
paste0("immune.",immune.clusters[4]),
paste0("immune.",immune.clusters[5]),
paste0("immune.",immune.clusters[6]),
paste0("immune.",immune.clusters[7]),
paste0("immune.",immune.clusters[8]),
paste0("immune.",immune.clusters[9])))
}else if (num.immune.clusters == 10) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3]),
paste0("immune.",immune.clusters[4]),
paste0("immune.",immune.clusters[5]),
paste0("immune.",immune.clusters[6]),
paste0("immune.",immune.clusters[7]),
paste0("immune.",immune.clusters[8]),
paste0("immune.",immune.clusters[9]),
paste0("immune.",immune.clusters[10])))
}
# perform infercnv operations to reveal cnv signal
infercnv_obj = infercnv::run(infercnv_obj,
cutoff=0.1, # use 1 for smart-seq, 0.1 for 10x-genomics
out_dir="./output_dir_CNV_postdoublet_PassedPC1Checks", # dir is auto-created for storing outputs
cluster_by_groups=T, # cluster
denoise=T,scale_data = T,
HMM=T,HMM_type = "i6",analysis_mode = "samples",min_cells_per_gene = 10,
BayesMaxPNormal = 0.4, num_threads = 8
)
regions <- read.delim("./output_dir_CNV_postdoublet_PassedPC1Checks/HMM_CNV_predictions.HMMi6.hmm_mode-samples.Pnorm_0.4.pred_cnv_regions.dat")
probs <- read.delim("./output_dir_CNV_postdoublet_PassedPC1Checks/BayesNetOutput.HMMi6.hmm_mode-samples/CNV_State_Probabilities.dat")
probs <- as.data.frame(t(probs[3,]))
colnames(probs) <- "Prob.Normal"
probs <- dplyr::filter(probs,Prob.Normal < 0.05)
cnvs <- rownames(probs)
cnvs <- gsub("\\.","-",cnvs)
regions <- regions[regions$cnv_name %in% cnvs, ]
cnv.groups <- sub("\\..*", "", regions$cell_group_name)
length(which(rownames(rna@[email protected]) == rownames([email protected])))
rna$PC1.loading <- rna@[email protected][,1]
rna$cell.barcode <- rownames([email protected])
rna$CNV.Pos <- ifelse(as.character(rna$postdoublet.idents) %in% cnv.groups,1,0)
cnv.freq <- data.frame(table(regions$cell_group_name))
cnv.freq$Var1 <- sub("\\..*", "", cnv.freq$Var1)
rna$Total_CNVs <- ifelse(as.character(rna$postdoublet.idents) %in% cnv.freq$Var1,cnv.freq$Freq,0)
boxplot.cnv <- ggplot([email protected],aes(x= postdoublet.idents,y=PC1.loading,color = as.factor(CNV.Pos)))+geom_boxplot()
boxplot.cnv+ggsave("postdoublet_CNV_PC1_boxplot.png")
data <- describeBy(boxplot.cnv$data$PC1.loading, boxplot.cnv$data$postdoublet.idents, mat = TRUE)
data$CNV <- ifelse(data$group1 %in% cnv.groups,1,0)
wilcox <- wilcox.test(data = [email protected],PC1.loading~CNV.Pos)
if (wilcox$p.value < 0.05){
rna <- rna
library(stringr)
levels(Idents(rna)) <- str_remove(levels(Idents(rna)),"immune.")
saveRDS(rna,"./rna_postdoublet_PassedPC1Checks.rds")
}else{
all.genes <- rownames(rna)
rna <- ScaleData(rna, features = all.genes,vars.to.regress = "nCount_RNA")
rna <- FindNeighbors(rna,dims = 1:50)
rna <- FindClusters(rna,resolution = 0.7)
rna <- RunUMAP(rna,dims = 1:50)
Idents(rna) <- "RNA_snn_res.0.7"
library(infercnv)
library(stringr)
library(Seurat)
counts_matrix = GetAssayData(rna, slot="counts")
# Identify immune clusters
#######################################################
# Find immune cells by relative enrichment of ESTIMATE immune signature
library(psych)
test <- VlnPlot(rna,features = "immune.2")
data <- describeBy(test$data$immune.2, test$data$ident, mat = TRUE)
data.immune <- dplyr::filter(data,median > 0.1)
test <- VlnPlot(rna,features = "plasma.7")
data <- describeBy(test$data$plasma.7, test$data$ident, mat = TRUE)
data.plasma <- dplyr::filter(data,median > 0.1)
immune.clusters <- intersect(data.immune$group1,levels(Idents(rna)))
plasma.clusters <- intersect(data.plasma$group1,levels(Idents(rna)))
immune.clusters <- unique(append(immune.clusters,plasma.clusters))
for (i in 1:length(immune.clusters)){
j <- which(levels(Idents(rna)) == immune.clusters[i])
levels(Idents(rna))[j] <- paste0("immune.",immune.clusters[i])
}
[email protected]$postdoublet.idents <- Idents(rna)
idents <- data.frame(rownames([email protected]),[email protected]$postdoublet.idents)
saveRDS(rna,"./rna_postdoublet_preinferCNV.rds")
#MAke inferCNV input
colnames(idents) <- c("V1","V2")
rownames(idents) <- NULL
colnames(idents) <- NULL
write.table(idents,"./sample_annotation_file_inferCNV.txt",sep = "\t",row.names = FALSE)
idents <- read.delim("./sample_annotation_file_inferCNV.txt",header = F)
gtf <- read.delim(GRCH38.annotations,header = F)
library(EnsDb.Hsapiens.v86)
convert.symbol = function(Data){
ensembls <- Data$V1
ensembls <- gsub("\\.[0-9]*$", "", ensembls)
geneIDs1 <- ensembldb::select(EnsDb.Hsapiens.v86, keys= ensembls, keytype = "GENEID", columns = "SYMBOL")
Data <- cbind.fill(Data, geneIDs1, fill = NA)
Data <- na.omit(Data)
Data$feature <- Data$SYMBOL
Data.new <- data.frame(Data$SYMBOL,Data$V2,Data$V3,Data$V4)
Data.new$Data.V2 <- paste("chr",Data.new$Data.V2,sep = "")
Data.new$Data.SYMBOL <- make.unique(Data.new$Data.SYMBOL)
return(Data.new)
}
gtf <- convert.symbol(gtf)
head(gtf)
write.table(gtf,"./Homo_sapiens.GRCh38.86.symbol.txt",sep = "\t",row.names = FALSE,col.names = FALSE)
num.immune.clusters = length(immune.clusters)
# create the infercnv object
if ( num.immune.clusters == 1) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=paste0("immune.",immune.clusters[1]))
} else if (num.immune.clusters == 2) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2])))
} else if ( num.immune.clusters == 3 ) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3])))
} else if (num.immune.clusters == 4) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3]),
paste0("immune.",immune.clusters[4])))
} else if (num.immune.clusters == 5) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3]),
paste0("immune.",immune.clusters[4]),
paste0("immune.",immune.clusters[5])))
} else if (num.immune.clusters == 6) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3]),
paste0("immune.",immune.clusters[4]),
paste0("immune.",immune.clusters[5]),
paste0("immune.",immune.clusters[6])))
}else if (num.immune.clusters == 7) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3]),
paste0("immune.",immune.clusters[4]),
paste0("immune.",immune.clusters[5]),
paste0("immune.",immune.clusters[6]),
paste0("immune.",immune.clusters[7])))
}else if (num.immune.clusters == 8) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3]),
paste0("immune.",immune.clusters[4]),
paste0("immune.",immune.clusters[5]),
paste0("immune.",immune.clusters[6]),
paste0("immune.",immune.clusters[7]),
paste0("immune.",immune.clusters[8])))
}else if (num.immune.clusters == 9) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3]),
paste0("immune.",immune.clusters[4]),
paste0("immune.",immune.clusters[5]),
paste0("immune.",immune.clusters[6]),
paste0("immune.",immune.clusters[7]),
paste0("immune.",immune.clusters[8]),
paste0("immune.",immune.clusters[9])))
}else if (num.immune.clusters == 10) {
infercnv_obj = CreateInfercnvObject(raw_counts_matrix=as.matrix(counts_matrix),
annotations_file="./sample_annotation_file_inferCNV.txt",
gene_order_file="./Homo_sapiens.GRCh38.86.symbol.txt",
ref_group_names=c(paste0("immune.",immune.clusters[1]),
paste0("immune.",immune.clusters[2]),
paste0("immune.",immune.clusters[3]),
paste0("immune.",immune.clusters[4]),
paste0("immune.",immune.clusters[5]),
paste0("immune.",immune.clusters[6]),
paste0("immune.",immune.clusters[7]),
paste0("immune.",immune.clusters[8]),
paste0("immune.",immune.clusters[9]),
paste0("immune.",immune.clusters[10])))
}
# perform infercnv operations to reveal cnv signal
infercnv_obj = infercnv::run(infercnv_obj,
cutoff=0.1, # use 1 for smart-seq, 0.1 for 10x-genomics
out_dir="./output_dir_CNV_postdoublet_FailedCNVTest", # dir is auto-created for storing outputs
cluster_by_groups=T, # cluster
denoise=T,scale_data = T,
HMM=T,HMM_type = "i6",analysis_mode = "samples",min_cells_per_gene = 10,
BayesMaxPNormal = 0.4, num_threads = 8
)
regions <- read.delim("./output_dir_CNV_postdoublet_FailedCNVTest/HMM_CNV_predictions.HMMi6.hmm_mode-samples.Pnorm_0.4.pred_cnv_regions.dat")
probs <- read.delim("./output_dir_CNV_postdoublet_FailedCNVTest/BayesNetOutput.HMMi6.hmm_mode-samples/CNV_State_Probabilities.dat")
probs <- as.data.frame(t(probs[3,]))
colnames(probs) <- "Prob.Normal"
probs <- dplyr::filter(probs,Prob.Normal < 0.05)
cnvs <- rownames(probs)
cnvs <- gsub("\\.","-",cnvs)
regions <- regions[regions$cnv_name %in% cnvs, ]
cnv.groups <- sub("\\..*", "", regions$cell_group_name)
length(which(rownames(rna@[email protected]) == rownames([email protected])))
rna$PC1.loading <- rna@[email protected][,1]
rna$cell.barcode <- rownames([email protected])
rna$CNV.Pos <- ifelse(as.character(rna$postdoublet.idents) %in% cnv.groups,1,0)
cnv.freq <- data.frame(table(regions$cell_group_name))
cnv.freq$Var1 <- sub("\\..*", "", cnv.freq$Var1)
rna$Total_CNVs <- ifelse(as.character(rna$postdoublet.idents) %in% cnv.freq$Var1,cnv.freq$Freq,0)
saveRDS(rna,"./rna_postdoublet_FailedCNVTest.rds")
}
}else{
all.genes <- rownames(rna)
rna <- ScaleData(rna, features = all.genes,vars.to.regress = "nCount_RNA")
rna <- FindNeighbors(rna,dims = 1:50)
rna <- FindClusters(rna,resolution = 0.7)