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03_HumanCellIntegration.R
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# devtools::install_github(repo = "satijalab/seurat", ref = "develop")
# Need to account for duplicate cell names...?
library(Seurat)
library(ggplot2)
options(future.globals.maxSize = 4000 * 1024^2)
################################################################################
# Load 10X data & Create Seurat Objects...
RdI_DMSO.data <- Read10X(
data.dir = "CellRangerOuts/RdI_DMSO_GRCh38/filtered_feature_bc_matrix/")
RdII_DMSO.data <- Read10X(
data.dir = "CellRangerOuts/RdII_DMSO_GRCh38/filtered_feature_bc_matrix/")
RdI_NM002.data <- Read10X(
data.dir = "CellRangerOuts/RdI_NM002_GRCh38/filtered_feature_bc_matrix/")
RdII_NM002.data <- Read10X(
data.dir = "CellRangerOuts/RdII_UM002_GRCh38/filtered_feature_bc_matrix/")
################################################################################
# DMSO
######
# Load in human cell barcode list from barnyard analysis
DMSO_human_barcodes <- as.character(read.csv(file = "02_output/DMSO_hu_barcodes.csv")$transfer)
head(DMSO_human_barcodes)
DMSO_human_barcodes <- paste0(DMSO_human_barcodes, "-1")
head(colnames(RdI_DMSO.data))
RdI_DMSO <- CreateSeuratObject(
counts = RdI_DMSO.data,
min.cells = 3,
min.features = 200,
project = "RdI_DMSO"
)
RdII_DMSO <- CreateSeuratObject(
counts = RdII_DMSO.data,
min.cells = 3,
min.features = 200,
project = "RdII_DMSO"
)
RdI_DMSO$Arm <- "DMSO"
RdII_DMSO$Arm <- "DMSO"
RdI_DMSO <- RenameCells(
object = RdI_DMSO,
new.names = paste0("DMSO_I_", colnames(x = RdI_DMSO))
)
RdII_DMSO <- RenameCells(
object = RdII_DMSO,
new.names = paste0("DMSO_II_", colnames(x = RdII_DMSO))
)
head(colnames(RdI_DMSO))
head(DMSO_human_barcodes)
RdI_DMSO <- subset(RdI_DMSO, cells = DMSO_human_barcodes)
RdII_DMSO <- subset(RdII_DMSO, cells = DMSO_human_barcodes)
# Check total # of cells matches number of barcodes...
length(DMSO_human_barcodes)
length(colnames(RdI_DMSO)) + length(colnames(RdII_DMSO))
# Cell Filtering QC
RdI_DMSO$percent.mt <- PercentageFeatureSet(RdI_DMSO, pattern = "^MT-")
RdII_DMSO$percent.mt <- PercentageFeatureSet(RdII_DMSO, pattern = "^MT-")
pdf(
file = "03_output/RdI_DMSOhu_prefilterQC.pdf"
)
FeatureScatter(object = RdI_DMSO, feature1 = "nCount_RNA", feature2 = "percent.mt")
FeatureScatter(object = RdI_DMSO, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
dev.off()
pdf(
file = "03_output/RdII_DMSOhu_prefilterQC.pdf"
)
FeatureScatter(object = RdII_DMSO, feature1 = "nCount_RNA", feature2 = "percent.mt")
FeatureScatter(object = RdII_DMSO, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
dev.off()
################################################################################
################################################################################
################################################################################
# NM002
#######
# Load in human cell barcode list from barnyard analysis
NM002_human_barcodes <- as.character(read.csv(file = "02_output/NM002_hu_barcodes.csv")$transfer)
head(NM002_human_barcodes)
NM002_human_barcodes <- paste0(NM002_human_barcodes, "-1")
head(colnames(RdI_NM002.data))
RdI_NM002 <- CreateSeuratObject(
counts = RdI_NM002.data,
min.cells = 3,
min.features = 200,
project = "RdI_NM002"
)
RdII_NM002 <- CreateSeuratObject(
counts = RdII_NM002.data,
min.cells = 3,
min.features = 200,
project = "RdII_NM002"
)
RdI_NM002$Arm <- "NM002"
RdII_NM002$Arm <- "NM002"
RdI_NM002 <- RenameCells(
object = RdI_NM002,
new.names = paste0("NM002_I_", colnames(x = RdI_NM002))
)
RdII_NM002 <- RenameCells(
object = RdII_NM002,
new.names = paste0("NM002_II_", colnames(x = RdII_NM002))
)
head(colnames(RdI_NM002))
head(NM002_human_barcodes)
RdI_NM002 <- subset(RdI_NM002, cells = NM002_human_barcodes)
RdII_NM002 <- subset(RdII_NM002, cells = NM002_human_barcodes)
# Check total # of cells matches number of barcodes...
length(NM002_human_barcodes)
length(colnames(RdI_NM002)) + length(colnames(RdII_NM002))
# Cell Filtering QC
RdI_NM002$percent.mt <- PercentageFeatureSet(RdI_NM002, pattern = "^MT-")
RdII_NM002$percent.mt <- PercentageFeatureSet(RdII_NM002, pattern = "^MT-")
pdf(
file = "03_output/RdI_NM002hu_prefilterQC.pdf"
)
FeatureScatter(object = RdI_NM002, feature1 = "nCount_RNA", feature2 = "percent.mt")
FeatureScatter(object = RdI_NM002, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
dev.off()
pdf(
file = "03_output/RdII_NM002hu_prefilterQC.pdf"
)
FeatureScatter(object = RdII_NM002, feature1 = "nCount_RNA", feature2 = "percent.mt")
FeatureScatter(object = RdII_NM002, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
dev.off()
################################################################################
################################################################################
################################################################################
## INTEGRATION
################################################################################
################################################################################
################################################################################
CellTagGBM.list <- c(RdI_DMSO, RdII_DMSO, RdI_NM002, RdII_NM002)
rm(RdI_DMSO, RdII_DMSO, RdI_NM002, RdII_NM002, RdI_DMSO.data, RdII_DMSO.data, RdI_NM002.data, RdII_NM002.data)
# And uniformly filter all datasets...
for (i in 1:length(CellTagGBM.list)) {
CellTagGBM.list[[i]] <- subset(x = CellTagGBM.list[[i]],
subset = nFeature_RNA > 200 & nFeature_RNA < 6000 & percent.mt < 10)
}
# Run scTransform on all objects...
for (i in 1:length(CellTagGBM.list)) {
CellTagGBM.list[[i]] <- SCTransform(CellTagGBM.list[[i]], verbose = TRUE, return.only.var.genes = FALSE)
}
obj.features <- SelectIntegrationFeatures(
object.list = CellTagGBM.list,
nfeatures = 3000
)
CellTagGBM.list <- PrepSCTIntegration(
object.list = CellTagGBM.list,
anchor.features = obj.features,
verbose = TRUE)
obj.anchors <- FindIntegrationAnchors(
object.list = CellTagGBM.list,
normalization.method = "SCT",
anchor.features = obj.features,
verbose = TRUE
)
to_integrate <- Reduce(intersect, lapply([email protected], rownames))
rm(CellTagGBM.list)
obj <- IntegrateData(
anchorset = obj.anchors,
features.to.integrate = to_integrate,
normalization.method = "SCT",
verbose = TRUE
)
rm(obj.anchors)
rm(obj.features)
rm(to_integrate)
saveRDS(obj, file = "03_output/CellTagHuInt.RDS")
# Now proceed with downstream analysis (i.e. visualization, clustering) on the
# integrated dataset. Commands are identical to the standard workflow, but do
# not run the ScaleData function after integration. This should have helped to
# remove any variation caused by technical batch effect.
################################################################################
################################################################################
################################################################################
obj <- RunPCA(obj, verbose = FALSE)
pdf(
file = "03_output/CellTagHuInt_ElbowPlot.pdf"
)
ElbowPlot(obj)
dev.off()
obj <- FindNeighbors(obj, dims = 1:15)
obj <- FindClusters(obj, dims = 1:15, resolution = seq(from = 0.9, to = 0.05, by = -0.05), algorithm = 2)
obj <- RunUMAP(
obj,
dims = 1:15,
min.dist = 0.5,
n.neighbors = 30,
n.epochs = 1000,
# umap.method = "uwot",
metric = "correlation",
assay = "integrated"
)
pdf(
file = "03_output/CellTagHuIntUMAP.pdf"
)
DimPlot(obj, group.by = c("Arm"))#, combine = FALSE)
DimPlot(obj, group.by = "ident", label = TRUE)#, combine = FALSE)
dev.off()
################################################################################
# Clustree Figure
library(clustree)
pdf(file = "03_output/CellTagHu_Clustree_Figures.pdf")
DimPlot(obj, group.by = "Arm")
clustree(obj, prefix = "integrated_snn_res.")
clustree(obj, prefix = "integrated_snn_res.", node_colour = "sc3_stability")
clustree(obj, prefix = "integrated_snn_res.", node_colour = "Suva.OPC.Signature1", node_colour_aggr = "mean")
clustree(obj, prefix = "integrated_snn_res.", node_colour = "Suva.NPC1.Signature1", node_colour_aggr = "mean")
clustree(obj, prefix = "integrated_snn_res.", node_colour = "Suva.NPC2.Signature1", node_colour_aggr = "mean")
clustree(obj, prefix = "integrated_snn_res.", node_colour = "Suva.MES1.Signature1", node_colour_aggr = "mean")
clustree(obj, prefix = "integrated_snn_res.", node_colour = "Suva.MES2.Signature1", node_colour_aggr = "mean")
clustree(obj, prefix = "integrated_snn_res.", node_colour = "Suva.AC.Signature1", node_colour_aggr = "mean")
clustree(obj, prefix = "integrated_snn_res.", node_colour = "Suva.G2_M.Signature1", node_colour_aggr = "mean")
clustree(obj, prefix = "integrated_snn_res.", node_colour = "Suva.G1_S.Signature1", node_colour_aggr = "mean")
dev.off()
################################################################################
################################################################################
################################################################################
saveRDS(obj, file = "03_output/CellTagHuInt_UMAP.Rds")
# obj <- readRDS(file = "03_output/obj_UMAP.Rds")
print("Pipeline successfully completed...")
################################################################################
################################################################################
################################################################################