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Script_8.R
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Script_8.R
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### 1. Load packages -----------------------------------------------------------
set.seed(1234)
library(Seurat)
library(ggplot2)
library(dplyr)
library(EnsDb.Hsapiens.v86)
library(BSgenome.Hsapiens.UCSC.hg38)
library(viridis)
library(Signac)
library(SoupX)
### 1. CHC2996N ----------------------------------------------------------------
sample_name <- "CHC2996N"
print(sample_name)
# Create useful directories paths-----------------------------------------------
data_dir <- paste0("//10.93.23.19/hepato partage/GENOMIC_DATA/scRNAseq/Project_HB_tumors/cellranger_outputs/PJ2302056_cellranger_v5/", sample_name, "/filtered_feature_bc_matrix/")
samp.directory <- paste0("D:/Dropbox/11p15.5 mosaicism/sn-RNAseq/samp_2996_3115_3559_4001/results/", sample_name, "/")
output.directory <- paste0(samp.directory, "preprocess.QC/")
if(!dir.exists(output.directory)){dir.create(output.directory)}
plots.dir.preQC <- paste0(output.directory, "plots/preQC/")
if(!dir.exists(plots.dir.preQC)){dir.create(plots.dir.preQC)}
plots.dir.postQC <- paste0(output.directory, "plots/postQC/")
if(!dir.exists(plots.dir.postQC)){dir.create(plots.dir.postQC)}
# load the RNA data-------------------------------------------------------------
list.files(data_dir) # Should show barcodes.tsv.gz, features.tsv.gz, and matrix.mtx.gz , atac_fragments.tsv.gz.tbi
data <- Read10X(data.dir = data_dir) # Load expression data
# get gene annotations for hg38-------------------------------------------------
hg38 <- GetGRangesFromEnsDb(ensdb = EnsDb.Hsapiens.v86)
genome(hg38) <- "hg38"
hg38 <- renameSeqlevels(hg38, mapSeqlevels(seqlevels(hg38), "UCSC"))
# create a Seurat object containing the RNA data--------------------------------
sample <- CreateSeuratObject(
counts = data,
assay = "RNA",
project = sample_name
)
# UMAP and clustering ---------------------------------------------------------
# colors and parameters --------------------------------------------------------
my_scale_exp = scale_colour_gradientn(colors = c( "gray70", "#ffff00", "red", "#cc0000"))
# Normalization with SCTransform -----------------------------------------------
DefaultAssay(sample) <- "RNA"
sample <- SCTransform(sample, verbose = TRUE, variable.features.n=3000) # sctransform = par d?faut 3000 nmostvar et pas 2000 comme lognormalize
sample <- ScaleData(sample, assay = "RNA")
DefaultAssay(sample) <- "SCT"
# PCA on gene expression--------------------------------------------------------
sample <- RunPCA(sample, features = VariableFeatures(sample), npcs=50) # 50 components
# Clustering --------------------------------------------------------------------
nbcomp=30
sample <- RunUMAP(sample, dims=1:nbcomp, verbose=F)
resolution_for_clusters <- 0.1 # change resolution to change nb of clusters determined
sample <- FindNeighbors(sample, dims = 1:30)
sample <- FindClusters(sample, resolution = 0.1)
sample$clusters_0_1 <- sample$seurat_clusters
# Plot UMAP --------------------------------------------------------------------
p<-DimPlot(sample, group.by = "clusters_0_1", reduction = "umap")
p
ggsave(paste0(plots.dir.preQC, sample_name, "_clusters_0_1_preQC.png"), plot = p, device = "png", width = 12, height = 10, units = "cm", dpi = 320)
# add mitochondrial percentage -------------------------------------------------
DefaultAssay(sample) <- "RNA"
sample[["percent.mt"]] <- PercentageFeatureSet(sample, pattern="^MT-")
# visualize QC in UMAP ---------------------------------------------------------
sample$nCount_RNA_log <- log10(sample$nCount_RNA)
sample$percent.mt_log <- log2(sample$percent.mt)
p<-FeaturePlot(sample, reduction="umap", features="percent.mt_log", pt.size = 0.1) + my_scale_exp & NoLegend()
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_percent_mt_log_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample, reduction="umap", features="nFeature_RNA", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_nFeature_RNA_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample, reduction="umap", features="nCount_RNA_log", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_nCount_RNA_log_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# Show specific markers to annotate cell types ---------------------------------
DefaultAssay(sample) <- "SCT"
p<-FeaturePlot(sample, reduction="umap", features="CD163", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_CD163_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample, reduction="umap", features="FLT1", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_CD163_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample, reduction="umap", features="KRT19", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_KRT19_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample, reduction="umap", features="KRT7", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_KRT7_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample, reduction="umap", features="CYP2E1", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_CYP2E1_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample, reduction="umap", features="HAL", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_HAL_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample, reduction="umap", features="HOXB3", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_HOXB3_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample, reduction="umap", features="VIM", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_VIM_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# Annotate cell types ----------------------------------------------------------
sample@meta.data <- sample@meta.data%>%
mutate(cell.type.old = case_when(clusters_0_1 =="0" ~ "High ambient RNA and MT genes",
clusters_0_1 == "1" ~ "Hepatocytes",
clusters_0_1 == "2" ~ "SAA2-CRP high hepatocytes/fibroblasts?",
clusters_0_1 == "3" ~ "Endothelial cells",
clusters_0_1 == "4" ~ "Macrophages",
clusters_0_1 == "5" ~ "Biliary epithelial cells",
clusters_0_1 == "6" ~ "HOX+ Progenitor",
clusters_0_1 == "7" ~ "Tumor cells"))
# Cell type colors -------------------------------------------------------------
cell.type.cols = c("Hepatocytes" = "#07dcf3",
"High ambient RNA and MT genes" = "#36e22e",
"Endothelial cells" = "#f3ef07",
"Macrophages" = "#357ee5",
"Biliary epithelial cells" = "#e53561",
"SAA2-CRP high hepatocytes/fibroblasts?" = "blue",
"HOX+ Progenitor" = "#ff9f2e",
"Tumor cells" = "#0c0994")
# Plot UMAP --------------------------------------------------------------------
p<-DimPlot(sample, group.by = "cell.type.old", reduction = "umap", cols = cell.type.cols)
p
ggsave(paste0(plots.dir.preQC, sample_name, "_clusters_0_1_cell_type_old_preQC.png"), plot = p, device = "png", width = 17, height = 10, units = "cm", dpi = 320)
# Differentially expressed genes ----------------------------------------------
sample.markers <- FindAllMarkers(object = sample, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.1)
top10 <- sample.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)
png(paste(plots.dir.preQC, sample_name, "_DoHeatmap_top10markers_preQC.png",sep=""),width=22,height=15,units="cm",res=1000)
print(DoHeatmap(sample, features = top10$gene,cells=colnames(sample), group.colors = cell.type.cols, group.by = "cell.type.old", size = 2) +
scale_fill_viridis(option="mako") +
theme(text = element_text(size = 6)) +
NoLegend())
dev.off()
write.table(sample.markers, quote = F,row.names = FALSE, paste(plots.dir.preQC, sample_name, "_allmarkers_preQC.txt",sep=""), sep="\t")
# Perform quality controls ---------------------------------------------------
p<-VlnPlot(
object = sample,
group.by = "cell.type.old",
features = c("percent.mt"),
cols = cell.type.cols,
pt.size = 0
) + geom_hline(yintercept=5)
p
ggsave(paste0(plots.dir.preQC, sample_name, "_MT_percentage_preQC.png"), plot = p, device = "png", width = 10, height = 15, units = "cm", dpi = 320)
p<-VlnPlot(
object = sample,
group.by = "cell.type.old",
features = c("percent.mt"),log = T,
cols = cell.type.cols,
pt.size = 0
) & NoLegend()
p
ggsave(paste0(plots.dir.preQC, sample_name, "_MT_percentage_log_preQC.png"), plot = p, device = "png", width = 10, height = 15, units = "cm", dpi = 320)
p<-VlnPlot(
object = sample,
group.by = "cell.type.old",
features = c("nCount_RNA_log"),
cols = cell.type.cols,
ncol = 1,
pt.size = 0) & NoLegend()
p
ggsave(paste0(plots.dir.preQC, sample_name, "_nCount_RNA_preQC.png"), plot = p, device = "png", width = 10, height = 15, units = "cm", dpi = 320)
p<-VlnPlot(
object = sample,
group.by = "cell.type.old",
features = c("nFeature_RNA"),
cols = cell.type.cols,
ncol = 1,
pt.size = 0) & NoLegend()
p
ggsave(paste0(plots.dir.preQC, sample_name, "_nFeature_RNA_preQC.png"), plot = p, device = "png", width = 10, height = 15, units = "cm", dpi = 320)
# Filter out low quality hepatocytes ------------------------------------------
sample.postqc <- subset(sample, subset = cell.type.old =="High ambient RNA and MT genes", invert=TRUE)
sample.postqc <- subset(
x = sample.postqc,
subset=nFeature_RNA>1000 & percent.mt<5)
print(length(colnames(sample)))
print(length(colnames(sample.postqc)))
# Remove doublets predicted with scrublet---------------------------------------
tmp_sobj <- Seurat::CreateSeuratObject(counts=Seurat::GetAssayData(sample.postqc,assay='RNA',slot="counts"))
tmp_sobj@assays$RNA@data <- Seurat::GetAssayData(sample.postqc,assay='RNA',slot="data")
tmp_sobj$orig.ident <- sample.postqc$orig.ident
sce <- Seurat::as.SingleCellExperiment(tmp_sobj)
sample.postqc$scDblFinder.class <- Seurat::as.Seurat(scDblFinder::scDblFinder(sce,samples=tmp_sobj$orig.ident))$scDblFinder.class
sample.postqc$scDblFinder.class <- unname(sample.postqc$scDblFinder.class == "doublet") # turn into TRUE/FALSE
p<-DimPlot(sample.postqc, group.by = "scDblFinder.class", cols = c("gray80","red"), reduction = "umap", raster=F)
p
ggsave(paste0(plots.dir.preQC, sample_name, "scDblFinder.class_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
print(length(colnames(sample.postqc)))
sample.postqc <- subset(
x = sample.postqc,
subset = scDblFinder.class=="FALSE")
print(length(colnames(sample.postqc)))
# Clustering --------------------------------------------------------------------
DefaultAssay(sample.postqc)<- "SCT"
sample.postqc <- RunPCA(sample.postqc, features = VariableFeatures(sample.postqc), npcs=30)
sample.postqc <- RunUMAP(sample.postqc, dims=1:30, verbose=T)
sample.postqc <- FindNeighbors(sample.postqc, dims = 1:30)
sample.postqc <- FindClusters(sample.postqc, resolution = 0.1)
sample.postqc$clusters_0_1_postQC <- sample.postqc$seurat_clusters
sample.postqc <- FindClusters(sample.postqc, resolution = 0.2)
sample.postqc$clusters_0_2_postQC <- sample.postqc$seurat_clusters
sample.postqc <- FindClusters(sample.postqc, resolution = 0.05)
sample.postqc$clusters_0_05_postQC <- sample.postqc$seurat_clusters
sample.postqc <- FindClusters(sample.postqc, resolution = 0.3)
sample.postqc$clusters_0_3_postQC <- sample.postqc$seurat_clusters
# Plot UMAP --------------------------------------------------------------------
p<-DimPlot(sample.postqc, group.by = "clusters_0_05_postQC", reduction = "umap", pt.size = 0.1)
p
ggsave(paste0(plots.dir.postQC, sample_name, "UMAP_clusters_0_05_postQC.png"), plot = p, device = "png", width = 10, height = 10, units = "cm", dpi = 320,)
p<-DimPlot(sample.postqc, group.by = "clusters_0_1_postQC", reduction = "umap", pt.size = 0.1)
p
ggsave(paste0(plots.dir.postQC, sample_name, "UMAP_clusters_0_1_postQC.png"), plot = p, device = "png", width = 10, height = 10, units = "cm", dpi = 320,)
p<-DimPlot(sample.postqc, group.by = "clusters_0_2_postQC", reduction = "umap", pt.size = 0.1)
p
ggsave(paste0(plots.dir.postQC, sample_name, "UMAP_clusters_0_2_postQC.png"), plot = p, device = "png", width = 10, height = 10, units = "cm", dpi = 320)
p<-DimPlot(sample.postqc, group.by = "clusters_0_3_postQC", reduction = "umap", pt.size = 0.1)
p
ggsave(paste0(plots.dir.postQC, sample_name, "UMAP_clusters_0_3_postQC.png"), plot = p, device = "png", width = 10, height = 10, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="percent.mt", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_percent_mt_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="nFeature_RNA", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_nFeature_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# visualize specific gene expression -------------------------------------------
DefaultAssay(sample.postqc) <- "SCT"
# endothelial cells-------------------------------------------------------------
p<-FeaturePlot(sample.postqc, reduction="umap", features="CD34", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_CD34_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="VWF", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_VWF_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="ANGPT2", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_ANGPT2_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="PTGDS", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_PTGDS_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="FLT1", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_FLT1_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="IGFBP3", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_IGFBP3_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="CDH5", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_CDH5_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="CLEC14A", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_CLEC14A_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="SLCO2A1", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_SLCO2A1_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="RELN", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_RELN_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="CLEC4M", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_CLEC4M_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="CLEC1B", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_CLEC1B_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="FCN2", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_FCN2_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="OIT3", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_OIT3_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# Hepatic stellate cells -------------------------------------------------------
p<-FeaturePlot(sample.postqc, reduction="umap", features="COLEC11", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_COLEC11_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="LRAT", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_LRAT_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="NGFR", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_NGFR_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# 11p-related genes ------------------------------------------------------------
p<-FeaturePlot(sample.postqc, reduction="umap", features="IGF2", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_IGF2_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="H19", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_H19_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# progenitor markers -----------------------------------------------------------
p<-FeaturePlot(sample.postqc, reduction="umap", features="GPC3", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_GPC3_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="DLK1", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_DLK1_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="EPCAM", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_EPCAM_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="KIT", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_KIT_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="AFP", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_AFP_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="LIN28B", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_LIN28B_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="THY1", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_THY1_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# Hematopoiesis ----------------------------------------------------------------
p<-FeaturePlot(sample.postqc, reduction="umap", features="HBB", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_HBB_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="ANK1", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_ANK1_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="HBM", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_HBM_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# Fibroblasts ------------------------------------------------------------------
p<-FeaturePlot(sample.postqc, reduction="umap", features="COL1A1", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_COL1A1_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="COL1A2", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_COL1A2_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="COL6A3", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_COL6A3_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="OGN", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_OGN_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="POSTN", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_POSTN_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="LUM", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_LUM_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="VIM", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_VIM_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="FN1", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_FN1_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# Macrophages-------------------------------------------------------------------
p<-FeaturePlot(sample.postqc, reduction="umap", features="CD163", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_CD163_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="CD68", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_CD68_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="CD80", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_CD80_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="C1QA", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_C1QA_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="C1QB", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_C1QB_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="C1QC", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_C1QC_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="CD74", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_CD74_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="HLA-DRA", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_HLA-DRA_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="LYZ", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_LYZ_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# B lymphocytes ----------------------------------------------------------------
p<-FeaturePlot(sample.postqc, reduction="umap", features="JCHAIN", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_JCHAIN_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="IGKC", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_IGKC_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="CD79A", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_CD79A_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="MZB1", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_MZB1_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# T lymphocytes ----------------------------------------------------------------
p<-FeaturePlot(sample.postqc, reduction="umap", features="CCL5", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_CCL5_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="GZMA", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_GZMA_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="GZMK", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_GZMK_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="CD3D", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_CD3D_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="NKG7", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_NKG7_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# NK cells ---------------------------------------------------------------------
p<-FeaturePlot(sample.postqc, reduction="umap", features="GNLY", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_GNLY_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="CD247", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_CD247_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="CD160", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_CD160_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="NCR1", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_NCR1_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# VSMC -------------------------------------------------------------------------
p<-FeaturePlot(sample.postqc, reduction="umap", features="TAGLN", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_TAGLN_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="MYH11", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_MYH11_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="ACTA2", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_ACTA2_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# Lipogenesis ------------------------------------------------------------------
p<-FeaturePlot(sample.postqc, reduction="umap", features="APOC1", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_APOC1_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="ALB", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_ALB_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="APOA2", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_APOA2_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# Platelets --------------------------------------------------------------------
p<-FeaturePlot(sample.postqc, reduction="umap", features="F2", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_F2_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# Periportal markers------------------------------------------------------------
p<-FeaturePlot(sample.postqc, reduction="umap", features="PCK1", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_PCK1_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="CPS1", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_CPS1_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="ASS1", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_ASS1_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="ASL", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_ASL_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="HAL", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_HAL_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# Perivenous markers------------------------------------------------------------
p<-FeaturePlot(sample.postqc, reduction="umap", features="CYP1A2", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_CYP1A2_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="CYP2E1", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_CYP2E1_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="OAT", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_OAT_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="GSTM3", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_GSTM3_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="NOTUM", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_NOTUM_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="CYP1A1", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_CYP1A1_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="FASN", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_FASN_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="DPP4", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_DPP4_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# Wnt pathway activation -------------------------------------------------------
p<-FeaturePlot(sample.postqc, reduction="umap", features="TBX3", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_TBX3_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="AXIN2", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_AXIN2_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="LGR5", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_LGR5_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="GLUL", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_GLUL_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# Biliary epithelial cells------------------------------------------------------
p<-FeaturePlot(sample.postqc, reduction="umap", features="KRT7", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_KRT7_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="KRT19", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_KRT19_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="SOX9", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_SOX9_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="SPP1", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_SPP1_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# Platelets --------------------------------------------------------------------
p<-FeaturePlot(sample.postqc, reduction="umap", features="ITGB2", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_ITGB2_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="SELP", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_SELP_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# Annotate cell types ----------------------------------------------------------
sample.postqc@meta.data <- sample.postqc@meta.data%>%
mutate(cell.type = case_when(clusters_0_2_postQC =="0" ~ "SAA2-CRP high hepatocytes/fibroblasts?",
clusters_0_2_postQC == "1" ~ "Hepatocytes",
clusters_0_2_postQC == "2" ~ "Hepatocytes",
clusters_0_2_postQC == "3" ~ "Endothelial cells",
clusters_0_2_postQC == "4" ~ "Biliary epithelial cells",
clusters_0_2_postQC == "5" ~ "Macrophages - Kupffer cells",
clusters_0_2_postQC == "6" ~ "Tumor cells",
clusters_0_2_postQC == "7" ~ "HOX+ Progenitor"))
# Cell type colors -------------------------------------------------------------
cell.type.cols = c("Hepatocytes" = "#07dcf3",
"Endothelial cells" = "#f3ef07",
"Macrophages - Kupffer cells" = "#357ee5",
"Biliary epithelial cells" = "#e53561",
"SAA2-CRP high hepatocytes/fibroblasts?" = "blue",
"HOX+ Progenitor" = "#ff9f2e",
"Tumor cells" = "#0c0994")
# Differentially expressed genes ----------------------------------------------
Idents(sample.postqc) <- sample.postqc@meta.data$cell.type
sample.markers <- FindAllMarkers(object = sample.postqc, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.1, group.by="cell.type")
top10 <- sample.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)
png(paste(plots.dir.postQC, sample_name, "_DoHeatmap_top10markers_postQC.png",sep=""),width=22,height=15,units="cm",res=1000)
print(DoHeatmap(sample.postqc, features = top10$gene, cells=colnames(sample.postqc), group.colors = cell.type.cols, group.by = "cell.type", size = 2) +
scale_fill_viridis(option="mako") +
theme(text = element_text(size = 6)) +
NoLegend())
dev.off()
write.table(sample.markers, quote = F,row.names = FALSE, paste(plots.dir.postQC, sample_name, "_allmarkers_postQC.txt",sep=""), sep="\t")
DefaultAssay(sample.postqc) <- "SCT"
# visualize QC after filtering--------------------------------------------------
p<-VlnPlot(
object = sample.postqc,
features = c("nCount_RNA","nFeature_RNA", "percent.mt"),
group.by = "cell.type",
cols=cell.type.cols,
ncol = 3,
pt.size = 0
) & NoLegend()
p
ggsave(paste0(plots.dir.postQC, sample_name, "_postQC_plots_postQC.png"), plot = p, device = "png", width = 22, height = 14, units = "cm", dpi = 320)
# Cell cycle scoring------------------------------------------------------------
# A list of cell cycle markers, from Tirosh et al, 2015, is loaded with Seurat. We can
# segregate this list into markers of G2/M phase and markers of S phase
DefaultAssay(sample.postqc) <- "RNA"
sample.postqc <- NormalizeData(sample.postqc)
s.genes <- cc.genes$s.genes
g2m.genes <- cc.genes$g2m.genes
length(s.genes)
setdiff(s.genes, rownames(sample.postqc))
s.genes <- s.genes[which(s.genes %in% rownames(sample.postqc))]
length(s.genes)
length(g2m.genes)
setdiff(g2m.genes, rownames(sample.postqc))
g2m.genes <- g2m.genes[which(g2m.genes %in% rownames(sample.postqc))]
length(g2m.genes)
sample.postqc <- CellCycleScoring(sample.postqc, s.features = s.genes, g2m.features = g2m.genes)
# view cell cycle scores and phase assignments----------------------------------
head(sample.postqc[[]])
p<-FeaturePlot(sample.postqc, reduction="umap", features="G2M.Score", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_G2M.Score_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample.postqc, reduction="umap", features="S.Score", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.postQC, sample_name, "_UMAP_S.Score_postQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-DimPlot(sample.postqc, group.by = "cell.type", reduction = "umap", cols = cell.type.cols, pt.size = 0.1)
p
ggsave(paste0(plots.dir.postQC, sample_name, "UMAP_cell_type.png"), plot = p, device = "png", width = 18, height = 10, units = "cm", dpi = 320,)
saveRDS(sample.postqc, file=paste0(output.directory, sample_name, "postQC.rds"))
saveRDS(sample, file=paste0(output.directory, sample_name, "preQC.rds"))
### 2. CHC3559N ----------------------------------------------------------------
sample_name <- "CHC3559N"
print(sample_name)
# Create useful directories paths-----------------------------------------------
data_dir <- paste0("//10.93.23.19/hepato partage/GENOMIC_DATA/scRNAseq/Project_HB_tumors/cellranger_outputs/PJ2302056_cellranger_v5/", sample_name, "/filtered_feature_bc_matrix/")
samp.directory <- paste0("D:/Dropbox/11p15.5 mosaicism/sn-RNAseq/samp_2996_3115_3559_4001/results/", sample_name, "/")
output.directory <- paste0(samp.directory, "preprocess.QC/")
if(!dir.exists(output.directory)){dir.create(output.directory)}
plots.dir.preQC <- paste0(output.directory, "plots/preQC/")
if(!dir.exists(plots.dir.preQC)){dir.create(plots.dir.preQC)}
plots.dir.postQC <- paste0(output.directory, "plots/postQC/")
if(!dir.exists(plots.dir.postQC)){dir.create(plots.dir.postQC)}
# load the RNA data-------------------------------------------------------------
list.files(data_dir) # Should show barcodes.tsv.gz, features.tsv.gz, and matrix.mtx.gz , atac_fragments.tsv.gz.tbi
data <- Read10X(data.dir = data_dir) # Load expression data
# get gene annotations for hg38-------------------------------------------------
hg38 <- GetGRangesFromEnsDb(ensdb = EnsDb.Hsapiens.v86)
genome(hg38) <- "hg38"
hg38 <- renameSeqlevels(hg38, mapSeqlevels(seqlevels(hg38), "UCSC"))
# create a Seurat object containing the RNA data--------------------------------
sample <- CreateSeuratObject(
counts = data,
assay = "RNA",
project = sample_name
)
# UMAP and clustering ---------------------------------------------------------
# colors and parameters --------------------------------------------------------
my_scale_exp = scale_colour_gradientn(colors = c( "gray70", "#ffff00", "red", "#cc0000"))
# Normalization with SCTransform -----------------------------------------------
DefaultAssay(sample) <- "RNA"
sample <- SCTransform(sample, verbose = TRUE, variable.features.n=3000) # sctransform = par d?faut 3000 nmostvar et pas 2000 comme lognormalize
sample <- ScaleData(sample, assay = "RNA")
DefaultAssay(sample) <- "SCT"
# PCA on gene expression--------------------------------------------------------
sample <- RunPCA(sample, features = VariableFeatures(sample), npcs=50) # 50 components
# Clustering --------------------------------------------------------------------
nbcomp=30
sample <- RunUMAP(sample, dims=1:nbcomp, verbose=F)
sample <- FindNeighbors(sample, dims = 1:30, k.param = 20)
sample <- FindClusters(sample, resolution = 0.1)
sample$clusters_0_1 <- sample$seurat_clusters
sample <- FindClusters(sample, resolution = 0.2)
sample$clusters_0_2 <- sample$seurat_clusters
sample <- FindClusters(sample, resolution = 0.3)
sample$clusters_0_3 <- sample$seurat_clusters
# Plot UMAP --------------------------------------------------------------------
p<-DimPlot(sample, group.by = "clusters_0_1", reduction = "umap")
p
ggsave(paste0(plots.dir.preQC, sample_name, "_clusters_0_1_preQC.png"), plot = p, device = "png", width = 12, height = 10, units = "cm", dpi = 320)
p<-DimPlot(sample, group.by = "clusters_0_2", reduction = "umap")
p
ggsave(paste0(plots.dir.preQC, sample_name, "_clusters_0_2_preQC.png"), plot = p, device = "png", width = 12, height = 10, units = "cm", dpi = 320)
p<-DimPlot(sample, group.by = "clusters_0_3", reduction = "umap")
p
ggsave(paste0(plots.dir.preQC, sample_name, "_clusters_0_3_preQC.png"), plot = p, device = "png", width = 12, height = 10, units = "cm", dpi = 320)
# add mitochondrial percentage -------------------------------------------------
DefaultAssay(sample) <- "RNA"
sample[["percent.mt"]] <- PercentageFeatureSet(sample, pattern="^MT-")
# visualize QC in UMAP ---------------------------------------------------------
sample$nCount_RNA_log <- log10(sample$nCount_RNA)
sample$percent.mt_log <- log2(sample$percent.mt)
p<-FeaturePlot(sample, reduction="umap", features="percent.mt_log", pt.size = 0.1) + my_scale_exp & NoLegend()
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_percent_mt_log_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample, reduction="umap", features="nFeature_RNA", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_nFeature_RNA_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample, reduction="umap", features="nCount_RNA_log", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_nCount_RNA_log_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# Show specific markers to annotate cell types ---------------------------------
DefaultAssay(sample) <- "SCT"
p<-FeaturePlot(sample, reduction="umap", features="CD163", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_CD163_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample, reduction="umap", features="FLT1", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_FLT1_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample, reduction="umap", features="KRT19", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_KRT19_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample, reduction="umap", features="KRT7", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_KRT7_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample, reduction="umap", features="CYP2E1", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_CYP2E1_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample, reduction="umap", features="HAL", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_HAL_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample, reduction="umap", features="HOXB3", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_HOXB3_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
p<-FeaturePlot(sample, reduction="umap", features="VIM", pt.size = 0.1) + my_scale_exp
p
ggsave(paste0(plots.dir.preQC, sample_name, "_UMAP_VIM_preQC.png"), plot = p, device = "png", width = 10, height = 8, units = "cm", dpi = 320)
# Annotate cell types ----------------------------------------------------------
sample@meta.data <- sample@meta.data%>%
mutate(cell.type.old = case_when(clusters_0_3 =="0" ~ "High ambient RNA and MT genes",
clusters_0_3 == "1" ~ "Periportal bifurcations cells ?",
clusters_0_3 == "2" ~ "Hepatocytes",
clusters_0_3 == "3" ~ "Endothelial cells",
clusters_0_3 == "4" ~ "Macrophages",
clusters_0_3 == "5" ~ "Fibroblasts",
clusters_0_3 == "6" ~ "HOX+ Progenitor",
clusters_0_3 == "7" ~ "Biliary epithelial cells"))
# Cell type colors -------------------------------------------------------------
cell.type.cols = c("Hepatocytes" = "#07dcf3",
"High ambient RNA and MT genes" = "#36e22e",
"Endothelial cells" = "#f3ef07",
"Macrophages" = "#357ee5",
"Fibroblasts" = "#8f35e5",
"Biliary epithelial cells" = "#e53561",
"HOX+ Progenitor" = "#ff9f2e",
"Periportal bifurcations cells ?" = "forestgreen")
# Plot UMAP --------------------------------------------------------------------
p<-DimPlot(sample, group.by = "cell.type.old", reduction = "umap", cols = cell.type.cols)
p
ggsave(paste0(plots.dir.preQC, sample_name, "_clusters_0_3_cell_type_old_preQC.png"), plot = p, device = "png", width = 17, height = 10, units = "cm", dpi = 320)
# Differentially expressed genes ----------------------------------------------
sample.markers <- FindAllMarkers(object = sample, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.1)
top10 <- sample.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)
png(paste(plots.dir.preQC, sample_name, "_DoHeatmap_top10markers_preQC.png",sep=""),width=22,height=15,units="cm",res=1000)
print(DoHeatmap(sample, features = top10$gene,cells=colnames(sample), group.colors = cell.type.cols, group.by = "cell.type.old", size = 2) +
scale_fill_viridis(option="mako") +
theme(text = element_text(size = 6)) +
NoLegend())
dev.off()
write.table(sample.markers, quote = F,row.names = FALSE, paste(plots.dir.preQC, sample_name, "_allmarkers_preQC.txt",sep=""), sep="\t")
# Perform quality controls ---------------------------------------------------
p<-VlnPlot(
object = sample,
group.by = "cell.type.old",
features = c("percent.mt"),