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HGSOC_ATAC.R
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HGSOC_ATAC.R
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###########################################################
# Matt Regner
# Franco Lab
# Date: May-December 2020
#
# Sample: HGSOC
# Description: This script performs the following tasks
# 1) scATAC-seq processing
# 2) scRNA-seq/scATAC-seq integration
# 3) Peak2Gene linkage analysis/Co-accessiblity
# 4) Make input files for TFSEE analysis
###########################################################
.libPaths('/home/regnerm/anaconda3/envs/scENDO_scOVAR/lib/R/library')
source("./Archr_Peak_Null.R")
source("./filterDoublets_modified.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(Signac)
library(ggplot2)
library(stringr)
library(EnsDb.Hsapiens.v86)
library(Seurat)
library(Signac)
library(ggplot2)
library(ensembldb)
library(EnsDb.Hsapiens.v86)
library(ArchR)
library(SingleR)
library(viridis)
set.seed(2)
addArchRThreads(threads = 16)
addArchRGenome("hg38")
# Set up directories and file variables:
##################################################################################################
SAMPLE.ID <- "HGSOC"
output.dir <- "."
####################
setwd(output.dir)
####################
inputFiles <- list.files(pattern = "\\.gz$")
sampleNames <- c("3BAE2L","3E5CFL")
encode.all <- read.delim("./GRCh38-ccREs.bed",header =F)
colnames(encode.all)[1:3] <- c("seqnames","start","end")
# Read in matching scRNA-seq
###################################################################################################
rna <- readRDS("./ovar_HGSOC_scRNA_processed.rds")
rna$cell.type <- str_replace(rna$cell.type,"/","_")
Idents(rna) <- "cell.type"
# Name cancer cell populations of interest (mostly epithelial with CNV)
labels <- c("3-Epithelial cell","15-Empty_Epithelial cell","2-Epithelial cell","19-Epithelial cell",
"0-Epithelial cell","11-Epithelial cell","7-Epithelial cell","16-Epithelial cell")
# Redo differential expression with new cell type markers
Wilcox.markers <- readRDS("./wilcox_DEGs.rds")
Wilcox.markers$cluster <- str_replace(Wilcox.markers$cluster,"/","_")
# Create Arrow and ArchR project
##########################################################################
ArrowFiles <- createArrowFiles(
inputFiles = inputFiles,
sampleNames = sampleNames,
filterTSS = 0, #Dont set this too high because you can always increase later
filterFrags = 0,
addTileMat = T,
addGeneScoreMat = F
)
doubScores <- addDoubletScores(
input = ArrowFiles,
k = 10, #Refers to how many cells near a "pseudo-doublet" to count.
knnMethod = "UMAP",useMatrix = "TileMatrix",nTrials=5,LSIMethod = 1,scaleDims = F,
corCutOff = 0.75,UMAPParams = list(n_neighbors =30, min_dist = 0.3, metric = "cosine", verbose =FALSE),
dimsToUse = 1:50
)
proj <- ArchRProject(
ArrowFiles = ArrowFiles,
outputDirectory = SAMPLE.ID,
copyArrows = T #This is recommened so that you maintain an unaltered copy for later usage.
)
# Filter out outlier low quality cells and doublets
###############################################################################
# GMM for fragments per cell
library(mclust)
for (i in sampleNames){
proj.i <- proj[proj$Sample == i]
# GMM for fragments per cell
depth.clust <- Mclust(log10(proj.i$nFrags),G = 2)
proj.i$depth.cluster <- depth.clust$classification
proj.i$depth.cluster.uncertainty <- depth.clust$uncertainty
ggPoint(
x = log10(proj.i$nFrags),
y = log10(proj.i$TSSEnrichment+1),
color = as.character(proj.i$depth.cluster),
xlabel = "log10(unique fragments)",
ylabel = "log10(TSS Enrichment+1)"
) + ggtitle(paste0("GMM classification:\n",i," log10(fragments)"))+
ggsave(paste0(i,"_depth.pdf"),width = 4,height = 4)
# GMM for TSS per cell
TSS.clust <- Mclust(log10(proj.i$TSSEnrichment+1),G = 2)
proj.i$TSS.cluster <- TSS.clust$classification
proj.i$TSS.cluster.uncertainty <- TSS.clust$uncertainty
ggPoint(
x = log10(proj.i$nFrags),
y = log10(proj.i$TSSEnrichment+1),
color = as.character(proj.i$TSS.cluster),
discrete = T,
xlabel = "log10(unique fragments)",
ylabel = "log10(TSS Enrichment+1)"
) + ggtitle(paste0("GMM classification:\n",i," TSS Enrichment"))+
ggsave(paste0(i,"_TSS.pdf"),width = 4,height = 4)
df.TSS <- data.frame(proj.i$cellNames,proj.i$TSS.cluster,proj.i$TSS.cluster.uncertainty,proj.i$TSSEnrichment)
df.TSS <- dplyr::filter(df.TSS,proj.i.TSS.cluster == "2")
df.TSS <- dplyr::filter(df.TSS,proj.i.TSS.cluster.uncertainty <= 0.05)
saveRDS(df.TSS,paste0("df_TSS_",i,".rds"))
df.depth <- data.frame(proj.i$cellNames,proj.i$depth.cluster,proj.i$depth.cluster.uncertainty,proj.i$nFrags)
df.depth <- dplyr::filter(df.depth,proj.i.depth.cluster == "2")
df.depth <- dplyr::filter(df.depth,proj.i.depth.cluster.uncertainty <= 0.05)
saveRDS(df.depth,paste0("df_depth_",i,".rds"))
ggPoint(
x = log10(proj.i$nFrags),
y = log10(proj.i$TSSEnrichment+1),
colorDensity = T,
continuousSet = "sambaNight",
xlabel = "log10(unique fragments)",
ylabel = "log10(TSS Enrichment+1)"
) +geom_hline(yintercept = log10(min(df.TSS$proj.i.TSSEnrichment)+1),linetype = "dashed")+
geom_vline(xintercept = min(log10(df.depth$proj.i.nFrags)),linetype = "dashed")+
ggtitle(paste0("QC thresholds:\n",i))+
ggsave(paste0(i,"_QC.pdf"),width = 4,height = 4)
ggPoint(
x = log10(proj.i$nFrags),
y = log10(proj.i$TSSEnrichment+1),
color = proj.i$DoubletEnrichment,
discrete = F,
continuousSet = "sambaNight",
xlabel = "log10(unique fragments)",
ylabel = "log10(TSS Enrichment+1)"
) +geom_hline(yintercept = min(log10(df.TSS$proj.i.TSSEnrichment+1)),linetype = "dashed")+
geom_vline(xintercept = min(log10(df.depth$proj.i.nFrags)),linetype = "dashed")+
ggtitle(paste0("Doublet Enrichment:\n",i))+
ggsave(paste0(i,"_doublets.pdf"),width = 4,height = 4)
}
###############################################################################
dev.off()
# Filter out low quality cells, and remove doublets
##############################################################################
list.depth <- list.files(pattern = "^df_depth")
df.depth <- data.frame(cellNames=character(),
cluster=character(),
cluster.uncertainty=character(),
nFrags = character())
for (i in list.depth){
df <- readRDS(i)
colnames(df) <- c("cellNames","cluster","cluster.uncertainty","nFrags")
df.depth <- rbind(df.depth,df)
}
list.TSS <- list.files(pattern = "^df_TSS")
df.TSS <- data.frame(cellNames=character(),
cluster=character(),
cluster.uncertainty=character(),
TSSEnrichment = character())
for (i in list.TSS){
df <- readRDS(i)
colnames(df) <- c("cellNames","cluster","cluster.uncertainty","TSSEnrichment")
df.TSS <- rbind(df.TSS,df)
}
colnames(df.TSS) <- c("cellNames","TSS.cluster","TSS.cluster.uncertainty","TSSEnrichment")
colnames(df.depth) <- c("cellNames","depth.cluster","depth.cluster.uncertainty","nFrags")
cellsPass <- intersect(df.TSS$cellNames,df.depth$cellNames)
cellsFail <- proj$cellNames[!(proj$cellNames %in% cellsPass)]
# Screen for high quality barcodes (remove non cellular barcodes)
proj.filter <- proj[proj$cellNames %in% cellsPass]
proj <- filterDoublets(proj.filter,filterRatio = 1,cutEnrich = 1,cutScore = -Inf)
plotFragmentSizes(proj)+ggtitle("Fragment Size Histogram")+ggsave("Frags_hist.pdf",width = 6,height = 4)
plotTSSEnrichment(proj)+ggtitle("TSS Enrichment")+ggsave("TSS.pdf",width = 6,height = 4)
###############################################################################################################
# Perform LSI reduction and clustering with ATAC data only
#######################################################################
# Add LSI dimreduc
proj <- addIterativeLSI(
ArchRProj = proj,
useMatrix = "TileMatrix",
name = "IterativeLSI",
iterations = 2,
LSIMethod = 2,
scaleDims = T,
clusterParams = list( #See Seurat::FindClusters
resolution = c(0.2),
sampleCells = 10000,
n.start = 10
),
UMAPParams = list(n_neighbors =30,
min_dist = 0.3,
metric = "cosine",
verbose =FALSE),
varFeatures = 25000,
dimsToUse = 1:50,
binarize = T,
corCutOff = 0.75,
force = T
)
proj <- addClusters(
input = proj,
reducedDims = "IterativeLSI",
method = "Seurat",
name = "ATAC_clusters",
resolution = 0.4,
dimsToUse = 1:50,force = T
)
# Add UMAP based on LSI dims
proj <- addUMAP(proj,nNeighbors = 30,minDist = 0.3,dimsToUse = 1:50,metric = "cosine",force = T)
###################################################################################################
# Estimate gene activity in ATAC data and perform cell type annotation:
# Add Gene activity matrix using ArchR model
proj <- addGeneScoreMatrix(proj,matrixName = "ArchRGeneScore",force = T)
# Add Gene activity matrix using Signac model
# gene.ranges <- ArchR::geneAnnoHg38
# gene.ranges <- gene.ranges$genes
# genebodyandpromoter.coords <- Signac::Extend(x = gene.ranges, upstream = 2000, downstream = 0)
# genebodyandpromoter.coords$name <- genebodyandpromoter.coords$symbol
# proj <- addFeatureMatrix(proj,matrixName = "SignacGeneScore",features = genebodyandpromoter.coords,force = T)
getAvailableMatrices(proj)
saveRDS(proj,"proj_LSI_AND_GeneScores.rds")
# Constrained Integration to only align cells from the same patient tumor
groupList <- SimpleList()
for (i in levels(factor(proj$Sample))){
rna.sub <- rna[,rna$Sample == i]
RNA.cells <- colnames(rna.sub)
idxSample <- BiocGenerics::which(proj$Sample == i)
cellsSample <- proj$cellNames[idxSample]
proj.filter <- proj[cellsSample, ]
ATAC.cells <- proj.filter$cellNames
groupList[[i]] <- SimpleList(
ATAC = ATAC.cells,
RNA = RNA.cells
)
}
# ###########################################################################################
# # Perform Seurat v3 label transfer between RNA/ATAC
# proj <- addGeneIntegrationMatrix(
# ArchRProj = proj,
# useMatrix = "SignacGeneScore",
# matrixName = "GeneIntegrationMatrix_Signac",
# reducedDims = "IterativeLSI",
# seRNA = rna,
# groupList = groupList,
# addToArrow = T,
# force= TRUE,
# groupRNA = "cell.type",
# nameCell = "predictedCell_Signac",
# nameGroup = "predictedGroup_Signac",
# nameScore = "predictedScore_Signac",
# plotUMAP = F,
# useImputation = F,
# transferParams = list(dims = 1:50)
# )
proj <- addGeneIntegrationMatrix(
ArchRProj = proj,
useMatrix = "ArchRGeneScore",
matrixName = "GeneIntegrationMatrix_ArchR",
reducedDims = "IterativeLSI",
seRNA = rna,
groupList = groupList,
addToArrow = T,
force= TRUE,
groupRNA = "cell.type",
nameCell = "predictedCell_ArchR",
nameGroup = "predictedGroup_ArchR",
nameScore = "predictedScore_ArchR",
plotUMAP = F,
useImputation = F,
transferParams = list(dims = 1:50)
)
getAvailableMatrices(proj)
saveRDS(proj,"proj_LSI_GeneScores_Annotations_Int.rds")
############################################################################################
# Begin Downstream Analysis
# 1) Plotting RNA/ATAC by sample, by cluster, by predicted label
# 2) Marker Gene (RNA/ATAC) intersection
# 3) Peak2GeneLinks/Coaccessiblity
######################################################
# PART 1: Plotting
######################################################################################
# Make embedding highlighting by 1) Predicted group ArchR 2) Predicted group Signac
# 3) Sample 4) ATAC-only clusters
atac.archr <- plotEmbedding(proj,colorBy = "cellColData",name = "predictedGroup_ArchR")
atac.archr.emb <- as.data.frame(atac.archr$data)
atac.archr.emb$cell.type.archr <- atac.archr.emb$color
atac.archr.emb$cell.type.archr <- sub("-", ":", atac.archr.emb$cell.type.archr)
atac.archr.emb$cell.type.archr <- gsub(".*:", "", atac.archr.emb$cell.type.archr)
head(atac.archr.emb)
atac.archr.emb$cell.type.archr <- factor(atac.archr.emb$cell.type.archr, levels = levels(as.factor(rna$cell.type)))
head(atac.archr.emb)
# atac.signac <- plotEmbedding(proj,colorBy = "cellColData",name = "predictedGroup_Signac")
# atac.signac.emb <- as.data.frame(atac.signac$data)
# atac.signac.emb$cell.type.signac <- atac.signac.emb$color
# atac.signac.emb$cell.type.signac <- sub("-", ":", atac.signac.emb$cell.type.signac)
# atac.signac.emb$cell.type.signac <- gsub(".*:", "", atac.signac.emb$cell.type.signac)
# head(atac.signac.emb)
# atac.signac.emb$cell.type.signac <- factor(atac.signac.emb$cell.type.signac, levels = levels(as.factor(rna$cell.type)))
# head(atac.signac.emb)
atac <- plotEmbedding(proj,colorBy = "cellColData",name = "Sample")
atac.emb.sample <- as.data.frame(atac$data)
atac.emb.sample$sample <- atac.emb.sample$color
atac.emb.sample$sample <- sub("-", ":", atac.emb.sample$sample )
atac.emb.sample$sample <- gsub(".*:", "", atac.emb.sample$sample )
head(atac.emb.sample)
head(atac.emb.sample)
atac <- plotEmbedding(proj,colorBy = "cellColData",name = "ATAC_clusters")
atac.emb.cluster <- as.data.frame(atac$data)
atac.emb.cluster$sample <- atac.emb.cluster$color
atac.emb.cluster$sample <- sub("-", ":", atac.emb.cluster$sample )
atac.emb.cluster$sample <- gsub(".*:", "", atac.emb.cluster$sample )
head(atac.emb.cluster)
atac.emb.all <- cbind(atac.archr.emb[,c(1:2,4)],
atac.emb.sample[,4],
atac.emb.cluster[,4])
atac.emb.all$plain <- "Plain"
colnames(atac.emb.all) <- c("UMAP1","UMAP2","Predicted.Group.ArchR",
"Sample","ATAC_clusters","Blank")
head(atac.emb.all)
var.list <- colnames(atac.emb.all)[3:4]
for (i in 1:length(var.list)){
ggplot(atac.emb.all,aes_string(x = "UMAP1",y="UMAP2",color = var.list[i]))+
geom_point(size = .1)+
theme_classic()+
ggtitle(paste0("scATAC-seq: ",var.list[i]))+
theme(plot.title = element_text(face = "bold"))+
xlab("UMAP_1")+
ylab("UMAP_2")+
theme(legend.key.size = unit(0.2, "cm"))+
guides(colour = guide_legend(override.aes = list(size=3)))+
ggsave(paste0(var.list[i],"_ATAC.pdf"),width = 8,height = 6)
}
prediction.scores <- data.frame(ArchR= proj$predictedScore_ArchR)
var.list <- colnames(prediction.scores)
for (i in 1:length(var.list)){
ggplot(prediction.scores,aes_string(x = var.list[i]))+
geom_histogram(binwidth = 0.025,fill="#000000", color="#e9ecef", alpha=0.9)+
theme_classic()+
ggsave(paste0(var.list[i],"_ATAC.pdf"),width = 8,height = 6)
}
# Plot matching scRNA-seq plots:
#####################################################################################
rna.emb <- as.data.frame(rna@[email protected])
rna.emb$cell.type <- as.factor(rna$cell.type)
rna.emb$sample <- rna$Sample
rna.emb$cell.type <- factor(rna.emb$cell.type,levels = levels(atac.emb.all$Predicted.Group.ArchR))
rna.cell.plot <- ggplot(rna.emb,aes(x = UMAP_1,y=UMAP_2,color = cell.type))+
geom_point(size = .1)+
theme_classic()+
ggtitle("scRNAseq")+
theme(plot.title = element_text(face = "bold"))+
xlab("UMAP_1")+
ylab("UMAP_2")+
theme(legend.key.size = unit(0.2, "cm"))+
guides(colour = guide_legend(override.aes = list(size=3)))
rna.cell.plot +ggsave("RNA_All_labels.pdf",width = 8,height = 6)
rna.emb$sample <- factor(rna.emb$sample,levels = levels(factor(atac.emb.all$Sample)))
rna.sample.plot <-ggplot(rna.emb,aes(x = UMAP_1,y=UMAP_2,color = sample))+
geom_point(size = .1)+
theme_classic()+
ggtitle("scRNAseq")+
theme(plot.title = element_text(face = "bold"))+
xlab("UMAP_1")+
ylab("UMAP_2")+
theme(legend.key.size = unit(0.2, "cm"))+
guides(colour = guide_legend(override.aes = list(size=3)))+ggsave("RNA_All_sample.pdf")
rna.sample.plot +ggsave("RNA_All_samples.pdf",width = 8,height = 6)
#########################################################################################################
# PART 2: Marker gene ovarlap RNA/ATAC
# Differential pseudo-gene activity analysis:
############################
######################################################################################
#ArchR
########################################################################################
# DEGs using ATAC labels
markersGS.archr <- getMarkerFeatures(
ArchRProj = proj,
useMatrix = "ArchRGeneScore",
groupBy = "ATAC_clusters",
bias = c("TSSEnrichment", "log10(nFrags)"),
testMethod = "wilcoxon"
)
heatmapGS.archr <- markerHeatmap(
seMarker = markersGS.archr,
cutOff = "FDR <= 0.01 & Log2FC >= 1.25",
labelMarkers =NULL,
transpose = F,
pal = viridis(n=256),
limits = c(-2,2)
)
ComplexHeatmap::draw(heatmapGS.archr, heatmap_legend_side = "bot", annotation_legend_side = "bot")
plotPDF(heatmapGS.archr , name = "GeneScores-Marker-Heatmap_ArchR", width = 8, height = 6, ArchRProj = proj, addDOC = FALSE)
# DEGs using predicted labels (removing small groups)
idxSample <- BiocGenerics::which(proj$predictedScore_ArchR > 0.5)
cellsSample <- proj$cellNames[idxSample]
proj.filter <- proj[cellsSample, ]
popular.groups <- summary(factor(proj.filter$predictedGroup_ArchR))
popular.groups <- popular.groups[popular.groups > 10]
proj.filter$Mode.Label <- ifelse(proj.filter$predictedGroup_ArchR %in% names(popular.groups),TRUE,FALSE)
idxSample <- BiocGenerics::which(proj.filter$Mode.Label == TRUE)
cellsSample <- proj.filter$cellNames[idxSample]
proj.filter <- proj.filter[cellsSample, ]
# DEGs using predicted labels
markersGS.archr.pred <- getMarkerFeatures(
ArchRProj = proj.filter,
useMatrix = "ArchRGeneScore",
groupBy = "predictedGroup_ArchR",
bias = c("TSSEnrichment", "log10(nFrags)"),
testMethod = "wilcoxon"
)
heatmapGS.archr.pred <- markerHeatmap(
seMarker = markersGS.archr.pred,
cutOff = "FDR <= 0.01 & Log2FC >= 1.25",
labelMarkers =NULL,
transpose = F,
pal = viridis(n=256),
limits = c(-2,2)
)
ComplexHeatmap::draw(heatmapGS.archr.pred, heatmap_legend_side = "bot", annotation_legend_side = "bot")
plotPDF(heatmapGS.archr.pred, name = "GeneScores-Marker-Heatmap_ArchR_pred", width = 8, height = 6, ArchRProj = proj.filter, addDOC = FALSE)
########################################################################################################################
#
#
# # Signac
# ########################################################################################################################
# # DEGs using ATAC labels
# markersGS.signac <- getMarkerFeatures(
# ArchRProj = proj,
# useMatrix = "SignacGeneScore",
# groupBy = "ATAC_clusters",
# bias = c("TSSEnrichment", "log10(nFrags)"),
# testMethod = "wilcoxon"
# )
#
#
# heatmapGS.signac <- markerHeatmap(
# seMarker = markersGS.signac,
# cutOff = "FDR <= 0.01 & Log2FC >= 1.25",
# labelMarkers =NULL,
# transpose = F,
# pal = viridis(n=256),
# limits = c(-2,2)
# )
# ComplexHeatmap::draw(heatmapGS.signac, heatmap_legend_side = "bot", annotation_legend_side = "bot")
# plotPDF(heatmapGS.signac , name = "GeneScores-Marker-Heatmap_signac", width = 8, height = 6, ArchRProj = proj, addDOC = FALSE)
#
#
#
#
# # DEGs using predicted labels (removing small groups)
# idxSample <- BiocGenerics::which(proj$predictedScore_Signac > 0.5)
# cellsSample <- proj$cellNames[idxSample]
# proj.filter <- proj[cellsSample, ]
#
# popular.groups <- summary(factor(proj.filter$predictedGroup_Signac))
# popular.groups <- popular.groups[popular.groups > 10]
# proj.filter$Mode.Label <- ifelse(proj.filter$predictedGroup_Signac %in% names(popular.groups),TRUE,FALSE)
#
# idxSample <- BiocGenerics::which(proj.filter$Mode.Label == TRUE)
# cellsSample <- proj.filter$cellNames[idxSample]
# proj.filter <- proj.filter[cellsSample, ]
#
# # DEGs using predicted labels
# markersGS.signac.pred <- getMarkerFeatures(
# ArchRProj = proj.filter,
# useMatrix = "SignacGeneScore",
# groupBy = "predictedGroup_Signac",
# bias = c("TSSEnrichment", "log10(nFrags)"),
# testMethod = "wilcoxon"
# )
# heatmapGS.signac.pred <- markerHeatmap(
# seMarker = markersGS.signac.pred,
# cutOff = "FDR <= 0.01 & Log2FC >= 1.25",
# labelMarkers =NULL,
# transpose = F,
# pal = viridis(n=256),
# limits = c(-2,2)
# )
# ComplexHeatmap::draw(heatmapGS.signac.pred, heatmap_legend_side = "bot", annotation_legend_side = "bot")
# plotPDF(heatmapGS.signac.pred, name = "GeneScores-Marker-Heatmap_Signac_pred", width = 8, height = 6, ArchRProj = proj.filter, addDOC = FALSE)
# Differential peak analysis:
############################
# ATAC clusters
proj <- addGroupCoverages(ArchRProj = proj,groupBy = "ATAC_clusters",force = T)
pathToMacs2 <- findMacs2()
proj <- addReproduciblePeakSet(
ArchRProj = proj,
groupBy = "ATAC_clusters",
pathToMacs2 = pathToMacs2,force = T
)
proj <- addPeakMatrix(proj,force = T)
proj <- addBgdPeaks(proj)
markersPeaks <- getMarkerFeatures(
ArchRProj = proj,
useMatrix = "PeakMatrix",
groupBy = "ATAC_clusters",
bias = c("TSSEnrichment", "log10(nFrags)"),
testMethod = "wilcoxon"
)
heatmapPeaks<- markerHeatmap(
seMarker = markersPeaks,
cutOff = "FDR <= 0.01 & Log2FC >= 1.25",
labelMarkers =NULL,
transpose = F,
pal = viridis(n=256),
limits = c(-2,2)
)
ComplexHeatmap::draw(heatmapPeaks, heatmap_legend_side = "bot", annotation_legend_side = "bot")
plotPDF(heatmapPeaks, name = "Markers_peaks_ATAC_clusters", width = 8, height = 6, ArchRProj = proj, addDOC = FALSE)
# ArchR predicted labels
# DEGs using predicted labels (removing small groups)
idxSample <- BiocGenerics::which(proj$predictedScore_ArchR >= 0.5)
cellsSample <- proj$cellNames[idxSample]
proj.filter <- proj[cellsSample, ]
popular.groups <- summary(factor(proj.filter$predictedGroup_ArchR))
popular.groups <- popular.groups[popular.groups > 30]
proj.filter$Mode.Label <- ifelse(proj.filter$predictedGroup_ArchR %in% names(popular.groups),TRUE,FALSE)
idxSample <- BiocGenerics::which(proj.filter$Mode.Label == TRUE)
cellsSample <- proj.filter$cellNames[idxSample]
proj.filter <- proj.filter[cellsSample, ]
proj.archr <- addGroupCoverages(ArchRProj = proj.filter,groupBy = "predictedGroup_ArchR",force = T)
pathToMacs2 <- findMacs2()
proj.archr <- addReproduciblePeakSet(
ArchRProj = proj.archr,
groupBy = "predictedGroup_ArchR",
pathToMacs2 = pathToMacs2,force = T
)
proj.archr <- addPeakMatrix(proj.archr,force = T)
proj.archr <- addBgdPeaks(proj.archr)
markersPeaks.archr <- getMarkerFeatures(
ArchRProj = proj.archr,
useMatrix = "PeakMatrix",
groupBy = "predictedGroup_ArchR",
bias = c("TSSEnrichment", "log10(nFrags)"),
testMethod = "wilcoxon"
)
heatmapPeaks.archr<- markerHeatmap(
seMarker = markersPeaks.archr,
cutOff = "FDR <= 0.01 & Log2FC >= 1.25",
labelMarkers =NULL,
transpose = F,
pal = viridis(n=256),
limits = c(-2,2)
)
ComplexHeatmap::draw(heatmapPeaks.archr, heatmap_legend_side = "bot", annotation_legend_side = "bot")
plotPDF(heatmapPeaks.archr , name = "Markers_peaks_Archr_Predicted_labels", width = 8, height = 6, ArchRProj = proj.archr, addDOC = FALSE)
#
#
# # Signac predicted labels
# # DEGs using predicted labels (removing small groups)
# idxSample <- BiocGenerics::which(proj$predictedScore_Signac > 0.5)
# cellsSample <- proj$cellNames[idxSample]
# proj.filter <- proj[cellsSample, ]
#
# popular.groups <- summary(factor(proj.filter$predictedGroup_Signac))
# popular.groups <- popular.groups[popular.groups > 10]
# proj.filter$Mode.Label <- ifelse(proj.filter$predictedGroup_Signac %in% names(popular.groups),TRUE,FALSE)
#
# idxSample <- BiocGenerics::which(proj.filter$Mode.Label == TRUE)
# cellsSample <- proj.filter$cellNames[idxSample]
# proj.filter <- proj.filter[cellsSample, ]
#
# proj.signac <- addGroupCoverages(ArchRProj = proj.filter,groupBy = "predictedGroup_Signac",force = T)
#
# pathToMacs2 <- findMacs2()
#
# proj.signac <- addReproduciblePeakSet(
# ArchRProj = proj.signac,
# groupBy = "predictedGroup_Signac",
# pathToMacs2 = pathToMacs2,force = T
# )
# proj.signac <- addPeakMatrix( proj.signac,force = T)
# proj.signac <- addBgdPeaks(proj.signac)
# markersPeaks.signac <- getMarkerFeatures(
# ArchRProj = proj.signac,
# useMatrix = "PeakMatrix",
# groupBy = "predictedGroup_Signac",
# bias = c("TSSEnrichment", "log10(nFrags)"),
# testMethod = "wilcoxon"
# )
#
# heatmapPeaks.signac <- markerHeatmap(
# seMarker = markersPeaks.signac,
# cutOff = "FDR <= 0.01 & Log2FC >= 1.25",
# labelMarkers =NULL,
# transpose = F,
# pal = viridis(n=256),
# limits = c(-2,2)
# )
# ComplexHeatmap::draw(heatmapPeaks.signac, heatmap_legend_side = "bot", annotation_legend_side = "bot")
# plotPDF(heatmapPeaks.signac, name = "Markers_peaks_Signac_Predicted_labels", width = 8, height = 6, ArchRProj = proj.signac, addDOC = FALSE)
#
# RNA heatmap
####################################################################################
topN <-Wilcox.markers %>% group_by(cluster) %>% dplyr::filter(p_val_adj <= 0.01) %>% top_n(30, desc(avg_logFC))
# Seurat Heatmap (top N DEGs per cluster)
#rna.sub <- subset(rna,downsample = 300)
#DoHeatmap(rna.sub,features = top30$gene,draw.lines = F,size = 2,disp.min = -2,disp.max = 2)+scale_fill_viridis()
## All 2000 variable features
#DoHeatmap(rna.sub,features = VariableFeatures(rna),draw.lines = F,size = 2,disp.min = -2,disp.max = 2)+scale_fill_viridis()
# Downsample cells from each cluster
rna.sub <- subset(rna,downsample =300)
rna.sub <- NormalizeData(rna.sub)
rna.sub <- rna.sub[rownames(rna.sub) %in% topN$gene,]
rna.sub <- ScaleData(rna.sub,features = rownames(rna.sub))
mat <- rna.sub@[email protected]
cluster_anno<- [email protected]$cell.type
# gg <- ggplot_build(rna.cell.plot)
#
# data <- as.data.frame(gg$data)
# names <- data$group
# names <- names -1
# data$group <- names
# #data$group <- data$colour
#
#
# cols <- unique(data$colour)
# names <- as.numeric(unique(data$group))
# names(cols) <- names
#
# cols <- cols[order(factor(names(cols), levels=levels(cluster_anno)))]
col_fun = circlize::colorRamp2(c(-2, 0, 2),viridis(n = 3))
heatmapRNA <- Heatmap(mat, name = "Expression",
column_split = factor(cluster_anno),
cluster_columns =T,
show_column_dend = F,
cluster_column_slices = T,
column_title_gp = gpar(fontsize = 8),
column_gap = unit(0.1, "mm"),
cluster_rows = T,
show_row_dend = FALSE,
col = col_fun,
column_title_rot = 90,
show_column_names = F)
plotPDF(heatmapRNA, name = "Heatmap_RNA", width = 8, height = 6)
##################################################################################################
source("./Archr_Peak_Null.R")
# Overlap RNA/ATAC DEG hits 1) ArchR 2) Signac
markerList.atac <- getMarkers(markersGS.archr.pred, cutOff = "FDR <= 0.01 & Log2FC >= 1.25")
Idents(rna) <- "cell.type"
Wilcox.markers <- readRDS("./wilcox_DEGs.rds")
Wilcox.markers$cluster <- str_replace(Wilcox.markers$cluster,"/","_")
# Make marker RNA list
for ( i in levels(factor(rna$cell.type))){
rna.cluster <- Wilcox.markers[Wilcox.markers$cluster == i &
Wilcox.markers$p_val_adj <= 0.01 &
Wilcox.markers$avg_logFC >= 1.0,]
saveRDS(rna.cluster,paste0("cluster_markers_",i,".rds"))
}
my.files <- list.files(pattern = "cluster_markers")
my.files
# now importing them all at once as entries in a list
# for my files, I have to provide some options to read.csv
markerList.rna <- lapply(my.files,
readRDS)
names <- str_remove(my.files,"cluster_markers_")
names(markerList.rna) <- str_remove(names,".rds")
int.names <- intersect(names(markerList.rna),names(markerList.atac))
gene.hits <- list()
for (i in int.names){
atac.clust <- markerList.atac[[i]]
rna.clust <- markerList.rna[[i]]
cluster.hits <- intersect(atac.clust$name,rna.clust$gene)
gene.hits[[i]] <- cluster.hits
}
########################################################################################
# Add Coaccessiblity and Peak2Gene links:
proj.archr <- addCoAccessibility(
ArchRProj = proj.archr,
reducedDims = "IterativeLSI",
dimsToUse = 1:50,
scaleDims = NULL,
corCutOff = 0.75,
k = 100,
knnIteration = 500,
overlapCutoff = 0.8,
maxDist = 1e+05,
scaleTo = 10^4,
log2Norm = TRUE,
seed = 1,
threads = getArchRThreads(),
verbose = TRUE,
logFile = createLogFile("addCoAccessibility")
)
proj.archr <- addPeak2GeneLinks(
ArchRProj = proj.archr ,
reducedDims = "IterativeLSI",
useMatrix = "GeneIntegrationMatrix_ArchR",
dimsToUse = 1:50,
scaleDims = NULL,
corCutOff = 0.75,
k = 100,
knnIteration = 500,
overlapCutoff = 0.8,
maxDist = 250000,
scaleTo = 10^4,
log2Norm = TRUE,
predictionCutoff = 0.5,
seed = 1,
threads = max(floor(getArchRThreads()/2), 1),
verbose = TRUE,
logFile = createLogFile("addPeak2GeneLinks")
)
# For every cluster, visualize the peaks coaccessible with marker genes
gg <- ggplot_build(rna.cell.plot)
data <- as.data.frame(gg$data)
data$cell.type <- rna.emb$cell.type
cols <- unique(data$colour)
names(cols) <- unique(data$cell.type)
for (i in names(gene.hits)){
genes <- gene.hits[[i]]
p <- plotBrowserTrack(
ArchRProj = proj.archr,
groupBy = "predictedGroup_ArchR",
geneSymbol = genes,
upstream = 50000,
downstream = 50000,
loops = getCoAccessibility(
ArchRProj = proj.archr,
corCutOff = 0.70,
resolution = 1,
returnLoops = TRUE
),
features = makeGRangesFromDataFrame(encode.all),
pal = cols
)
plotPDF(plotList = p,
name = paste0("ArchR_CoAscblty_Hits",i,".pdf"),
ArchRProj = proj.archr,
addDOC = T, width = 5, height = 5)
p <- plotBrowserTrack(
ArchRProj = proj.archr,
groupBy = "predictedGroup_ArchR",
geneSymbol = genes,
upstream = 50000,
downstream = 50000,
loops = getPeak2GeneLinks(
ArchRProj = proj.archr,
corCutOff = 0.70,
FDRCutOff = 1e-04,
resolution = 1,
returnLoops = TRUE
),
features = makeGRangesFromDataFrame(encode.all),
pal = cols
)
loops <- getPeak2GeneLinks(
ArchRProj = proj.archr,
corCutOff = 0.70,
FDRCutOff = 1e-04,
resolution = 1,
returnLoops = F
)
saveRDS(loops,paste0(i,"_empirical_pval_archr.rds"))
plotPDF(plotList = p,
name = paste0("ArchR_Peak2Gene_Hits_",i,".pdf"),
ArchRProj = proj.archr,
addDOC = T, width = 6, height = 8)
}
# source("./Archr_Peak_Null.R")
# # Overlap RNA/ATAC DEG hits
# markerList.atac <- getMarkers(markersGS.signac.pred, cutOff = "FDR <= 0.01 & Log2FC >= 1.25")
#
# # Make marker RNA list
# for ( i in levels(factor(rna$cell.type))){
# rna.cluster <- Wilcox.markers[Wilcox.markers$cluster == i &
# Wilcox.markers$p_val_adj <= 0.01 &
# Wilcox.markers$avg_logFC >= 1.0,]
# saveRDS(rna.cluster,paste0("cluster_markers_signac_",i,".rds"))
# }
#
# my.files <- list.files(pattern = "cluster_markers_signac_")
# my.files
#
#
# # now importing them all at once as entries in a list
# # for my files, I have to provide some options to read.csv
# markerList.rna <- lapply(my.files,
# readRDS)
#
# names <- str_remove(my.files,"cluster_markers_signac_")
# names(markerList.rna) <- str_remove(names,".rds")
#
#
# int.names <- intersect(names(markerList.rna),names(markerList.atac))
#
#
# gene.hits <- list()
# for (i in int.names){
#
# atac.clust <- markerList.atac[[i]]
# rna.clust <- markerList.rna[[i]]
# cluster.hits <- intersect(atac.clust$name,rna.clust$gene)
#
# gene.hits[[i]] <- cluster.hits
# }
# ########################################################################################
#
# # Add Coaccessiblity and Peak2Gene links:
# proj.signac <- addCoAccessibility(
# ArchRProj = proj.signac,
# reducedDims = "IterativeLSI",
# dimsToUse = 1:50,
# scaleDims = NULL,
# corCutOff = 0.75,
# k = 100,
# knnIteration = 500,
# overlapCutoff = 0.8,
# maxDist = 1e+05,
# scaleTo = 10^4,
# log2Norm = TRUE,
# seed = 1,
# threads = getArchRThreads(),
# verbose = TRUE,
# logFile = createLogFile("addCoAccessibility")
# )
#
# proj.signac <- addPeak2GeneLinks(
# ArchRProj = proj.signac ,
# reducedDims = "IterativeLSI",
# useMatrix = "GeneIntegrationMatrix_Signac",