--- title: "Liver Droplet- Raw data Notebook" output: html_notebook --- ```{r} tissue_of_interest = "Liver" library(here) source("/restricted/projectnb/waxmanlab/kkarri/scRNAseq_data_integration/boilerplate.R") #tiss = load_tissue_droplet(tissue_of_interest) #library(scater) library(dplyr) library(Seurat) library(cowplot) #library(MAST) require(stringr) require(reshape2) require(ggplot2) require(MASS) library(tools) require(data.table) library(ggfortify) library(tidyverse) require(dplyr) library(miscTools) library(caret) library(Rtsne) library(ggrepel) ``` G172 ```{r} G172.umis <- Read10X("/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G172/10X_TCPO_premRNA_Transcript/outs/filtered_feature_bc_matrix") #G172.umis.raw <- Read10X("/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G172/10X_TCPO_premRNA_Transcript/outs/raw_feature_bc_matrix/") G172.htos <- Read10X("/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G172/HASH_Citeseq/HASH_Results_filtered-10X-BC/umi_count", gene.column=1) # Select cell barcodes detected by both RNA and HTO In the example datasets we have already # filtered the cells for you, but perform this step for clarity. joint.bcs <- intersect(colnames(G172.umis), colnames(G172.htos)) # Subset RNA and HTO counts by joint cell barcodes joint_cite1_cite2 <- intersect(colnames(G172.htos), colnames(df2)) G172.umis <- G172.umis[, joint.bcs] G172.htos <- as.matrix(G172.htos[, joint.bcs]) G172.htos <- G172.htos[-5,] # Confirm that the HTO have the correct names rownames(G172.htos) # Setup Seurat object G172.hashtag <- CreateSeuratObject(counts = G172.umis) # Normalize RNA data with log normalization G172.hashtag <- NormalizeData(G172.hashtag) # Find and scale variable features G172.hashtag <- FindVariableFeatures(G172.hashtag, selection.method = "mean.var.plot") G172.hashtag <- ScaleData(G172.hashtag, features = VariableFeatures(G172.hashtag)) # Add HTO data as a new assay independent from RNA G172.hashtag[["HTO"]] <- CreateAssayObject(counts = G172.htos) # Normalize HTO data, here we use centered log-ratio (CLR) transformation G172.hashtag <- NormalizeData(G172.hashtag, assay = "HTO", normalization.method = "CLR") # If you have a very large dataset we suggest using k_function = 'clara'. This is a k-medoid # clustering function for large applications You can also play with additional parameters (see # documentation for HTODemux()) to adjust the threshold for classification Here we are using the # default settings G172.hashtag <- HTODemux(G172.hashtag, assay = "HTO", positive.quantile = 0.99, kfunc = 'clara') # Global classification results table(G172.hashtag$HTO_classification.global) table(G172.hashtag$hash.ID) # Group cells based on the max HTO signal Idents(G172.hashtag) <- "hash.ID" RidgePlot(G172.hashtag, assay = "HTO", features = rownames(G172.hashtag[["HTO"]])[1:4], ncol = 2, nrow=2) #Idents(G172.hashtag) <- "HTO_classification.global" # First, we will remove negative cells from the object G172.hashtag.subset <- subset(G172.hashtag, idents =c("Negative","Doublet"), invert = TRUE) ## Feature scatterplot for hastags IDs ######## FeatureScatter(G172.hashtag.subset, feature1 = "M1-ATGATGAACAGCCAG", feature2 = "M2-TGACGCCGTTGTTGT") FeatureScatter(G172.hashtag.subset, feature1 = "M1-ATGATGAACAGCCAG", feature2 = "M3-GCCTAGTATGATCCA") FeatureScatter(G172.hashtag.subset, feature1 = "M1-ATGATGAACAGCCAG", feature2 = "M4-AGTCACAGTATTCCA") FeatureScatter(G172.hashtag.subset, feature1 = "M2-TGACGCCGTTGTTGT", feature2 = "M3-GCCTAGTATGATCCA") FeatureScatter(G172.hashtag.subset, feature1 = "M2-TGACGCCGTTGTTGT", feature2 = "M4-AGTCACAGTATTCCA") FeatureScatter(G172.hashtag.subset, feature1 = "M3-GCCTAGTATGATCCA", feature2 = "M4-AGTCACAGTATTCCA") # Calculate a distance matrix using HTO hto.dist.mtx <- as.matrix(dist(t(GetAssayData(object = G172.hashtag.subset, assay = "HTO")))) # Calculate tSNE embeddings with a distance matrix G172.hashtag.subset <- RunTSNE(G172.hashtag.subset, distance.matrix = hto.dist.mtx, perplexity = 100) DimPlot(G172.hashtag.subset) HTOHeatmap(G172.hashtag, assay = "HTO", ncells = 26740) ########################## Rescue doublets ######################## G172.doublet <- subset(G172.hashtag, idents = "Doublet") G172.doublet.rescue <- HTODemux(G172.doublet, assay = "HTO", positive.quantile = 0.99, kfunc = 'clara') Idents(G172.doublet.rescue) <- "hash.ID" RidgePlot(G172.doublet.rescue, assay = "HTO", features = rownames(G172.doublet.rescue[["HTO"]])[1:4], ncol = 2, nrow=2) ############################################################################### # Extract the singlets M1 #############3 G172.M1 <- subset(G172.hashtag, idents = "M1-ATGATGAACAGCCAG", subset = nFeature_RNA > 500 & nCount_RNA > 1000) G172.M1$stim <- "G172M1" DefaultAssay(G172.M1) <- "RNA" G172.M1 <- SCTransform(G172.M1,verbose =TRUE) # Select the top 1000 most variable features G172.M1 <- NormalizeData(G172.M1, verbose = FALSE) G172.M1 <- FindVariableFeatures(G172.M1, selection.method = "vst", nfeatures = 2000) # Scaling RNA data, we only scale the variable features here for efficiency G172.M1 <- ScaleData(G172.M1, features = VariableFeatures(G172.M1)) # Run PCA G172.M1 <- RunPCA(G172.M1,npcs = 30, features = VariableFeatures(G172.M1)) # We select the top 10 PCs for clustering and tSNE based on PCElbowPlot G172.M1 <- RunUMAP(G172.M1, reduction = "pca", dims = 1:25) G172.M1 <- FindNeighbors(G172.M1, reduction = "pca", dims = 1:25) G172.M1 <- FindClusters(G172.M1, resolution = 0.6 ) G172.M1.p1<- UMAPPlot(G172.M1, reduction = "umap", label=TRUE, label.size=5) G172.M1.p1 DefaultAssay(G172.M1) <- "RNA" G172.M1 <- NormalizeData(G172.M1, verbose = TRUE) d1 <- DotPlot(G172.M1, features = all_genes)+RotatedAxis() plot_grid(G172.M1.p1,d1) # Extract the singlet for M2 #############3 G172.M2 <- subset(G172.hashtag, idents = "M2-TGACGCCGTTGTTGT", subset = nFeature_RNA > 500 & nCount_RNA > 1000) G172.M2$stim <- "G172M2" DefaultAssay(G172.M2) <- "RNA" G172.M2 <- SCTransform(G172.M2,verbose =TRUE) # Select the top 1000 most variable features G172.M2 <- NormalizeData(G172.M2, verbose = FALSE) G172.M2 <- FindVariableFeatures(G172.M2, selection.method = "vst", nfeatures = 2000) # Scaling RNA data, we only scale the variable features here for efficiency G172.M2 <- ScaleData(G172.M2, features = VariableFeatures(G172.M2)) # Run PCA G172.M2 <- RunPCA(G172.M2,npcs = 30, features = VariableFeatures(G172.M2)) # We select the top 10 PCs for clustering and tSNE based on PCElbowPlot G172.M2 <- RunUMAP(G172.M2, reduction = "pca", dims = 1:25) G172.M2 <- FindNeighbors(G172.M2, reduction = "pca", dims = 1:25) G172.M2 <- FindClusters(G172.M2, resolution = 0.5 ) G172.M2.p1<- UMAPPlot(G172.M2, reduction = "umap", label=TRUE, label.size=5) G172.M2.p1 DefaultAssay(G172.M2) <- "RNA" G172.M2 <- NormalizeData(G172.M2, verbose = TRUE) d2 <- DotPlot(G172.M2, features = all_genes)+RotatedAxis() plot_grid(G172.M2.p1,d2) #### Extract the singlet for M3 ##### G172.M3 <- subset(G172.hashtag, idents = "M3-GCCTAGTATGATCCA", subset = nFeature_RNA > 500 & nCount_RNA > 1000) DefaultAssay(G172.M3) <- "RNA" G172.M3 <- SCTransform(G172.M3,verbose =TRUE) # Select the top 1000 most variable features G172.M3 <- NormalizeData(G172.M3, verbose = FALSE) G172.M3 <- FindVariableFeatures(G172.M3, selection.method = "vst", nfeatures = 2000) # Scaling RNA data, we only scale the variable features here for efficiency G172.M3 <- ScaleData(G172.M3, features = VariableFeatures(G172.M3)) # Run PCA G172.M3 <- RunPCA(G172.M3,npcs = 30, features = VariableFeatures(G172.M3)) # We select the top 10 PCs for clustering and tSNE based on PCElbowPlot G172.M3 <- RunUMAP(G172.M3, reduction = "pca", dims = 1:25) G172.M3 <- FindNeighbors(G172.M3, reduction = "pca", dims = 1:25) G172.M3 <- FindClusters(G172.M3, resolution = 0.5 ) G172.M3.p1<- UMAPPlot(G172.M3, reduction = "umap", label=TRUE, label.size=5) G172.M3.p1 DefaultAssay(G172.M3) <- "RNA" G172.M3 <- NormalizeData(G172.M3, verbose = TRUE) d3 <- DotPlot(G172.M3, features = all_genes)+RotatedAxis() plot_grid(G172.M3.p1,d3) ##### Extract the singlet for M4 ##### G172.M4 <- subset(G172.hashtag, idents = "M4-AGTCACAGTATTCCA", subset = nFeature_RNA > 500 & nCount_RNA > 1000) DefaultAssay(G172.M4) <- "RNA" G172.M4 <- SCTransform(G172.M4,verbose =TRUE) # Select the top 1000 most variable features G172.M4 <- NormalizeData(G172.M4, verbose = FALSE) G172.M4 <- FindVariableFeatures(G172.M4, selection.method = "vst", nfeatures = 2000) # Scaling RNA data, we only scale the variable features here for efficiency G172.M4 <- ScaleData(G172.M4, features = VariableFeatures(G172.M4)) # Run PCA G172.M4 <- RunPCA(G172.M4,npcs = 30, features = VariableFeatures(G172.M4)) # We select the top 10 PCs for clustering and tSNE based on PCElbowPlot G172.M4 <- RunUMAP(G172.M4, reduction = "pca", dims = 1:25) G172.M4 <- FindNeighbors(G172.M4, reduction = "pca", dims = 1:25) G172.M4 <- FindClusters(G172.M4, resolution = 0.5 ) G172.M4.p1<- UMAPPlot(G172.M4, reduction = "umap", label=TRUE, label.size=5) G172.M4.p1 DefaultAssay(G172.M4) <- "RNA" G172.M4 <- NormalizeData(G172.M4, verbose = TRUE) d4 <- DotPlot(G172.M4, features = all_genes)+RotatedAxis() plot_grid(G172.M4.p1,d4) ############## Combined ####################33 G172.M5 <- subset(G172.hashtag, idents = c("M1-ATGATGAACAGCCAG","M2-TGACGCCGTTGTTGT","M3-GCCTAGTATGATCCA","M4-AGTCACAGTATTCCA"), subset = nFeature_RNA > 500 & nCount_RNA > 1000) DefaultAssay(G172.M5) <- "RNA" G172.M5 <- SCTransform(G172.M5,verbose =TRUE) # Select the top 1000 most variable features G172.M5 <- NormalizeData(G172.M5, verbose = FALSE) G172.M5 <- FindVariableFeatures(G172.M5, selection.method = "vst", nfeatures = 2000) # Scaling RNA data, we only scale the variable features here for efficiency G172.M5 <- ScaleData(G172.M5, features = VariableFeatures(G172.M5)) # Run PCA G172.M5 <- RunPCA(G172.M5,npcs = 30, features = VariableFeatures(G172.M5)) # We select the top 10 PCs for clustering and tSNE based on PCElbowPlot G172.M5 <- RunUMAP(G172.M5, reduction = "pca", dims = 1:25) G172.M5 <- FindNeighbors(G172.M5, reduction = "pca", dims = 1:25) G172.M5 <- FindClusters(G172.M5, resolution = 0.5 ) G172.M5.p1<- UMAPPlot(G172.M5, reduction = "umap", label=TRUE, label.size=5) G172.M5.p1 DefaultAssay(G172.M5) <- "RNA" G172.M5 <- NormalizeData(G172.M5, verbose = TRUE) d5 <- DotPlot(G172.M5, features = c(all_genes,'Cyp2b10','Cyp2d9'))+RotatedAxis() plot_grid(G172.M5.p1,d5) ### control G172.M6 <- subset(G172.hashtag.subset, idents = c("M1-ATGATGAACAGCCAG","M3-GCCTAGTATGATCCA"), subset = nFeature_RNA > 500 & nCount_RNA > 1000) DefaultAssay(G172.M6) <- "RNA" #G172.M6 <- SCTransform(G172.M6,verbose =TRUE) # Select the top 1000 most variable features G172.M6 <- NormalizeData(G172.M6, verbose = FALSE) G172.M6 <- FindVariableFeatures(G172.M6, selection.method = "vst", nfeatures = 2000) # Scaling RNA data, we only scale the variable features here for efficiency G172.M6 <- ScaleData(G172.M6, features = VariableFeatures(G172.M6)) # Run PCA G172.M6 <- RunPCA(G172.M6,npcs = 30, features = VariableFeatures(G172.M6)) # We select the top 10 PCs for clustering and tSNE based on PCElbowPlot G172.M6 <- RunUMAP(G172.M6, reduction = "pca", dims = 1:25) G172.M6 <- FindNeighbors(G172.M6, reduction = "pca", dims = 1:25) G172.M6 <- FindClusters(G172.M6, resolution = 0.5 ) G172.M6.p1<- UMAPPlot(G172.M6, reduction = "umap", label=TRUE, label.size=5) G172.M6.p1 DefaultAssay(G172.M6) <- "RNA" G172.M6 <- NormalizeData(G172.M6, verbose = TRUE) d6 <- DotPlot(G172.M6, features = c(all_genes,'Cyp2b10','Cyp2d9'))+RotatedAxis() plot_grid(G172.M6.p1,d6) #### TCPO G172.M7 <- subset(G172.hashtag, idents = c("M2-TGACGCCGTTGTTGT","M4-AGTCACAGTATTCCA"), subset = nFeature_RNA > 500 & nCount_RNA > 1000) DefaultAssay(G172.M7) <- "RNA" G172.M7 <- SCTransform(G172.M7,verbose =TRUE) # Select the top 1000 most variable features G172.M7 <- NormalizeData(G172.M7, verbose = FALSE) G172.M7 <- FindVariableFeatures(G172.M7, selection.method = "vst", nfeatures = 2000) # Scaling RNA data, we only scale the variable features here for efficiency G172.M7 <- ScaleData(G172.M7, features = VariableFeatures(G172.M7)) # Run PCA G172.M7 <- RunPCA(G172.M7,npcs = 30, features = VariableFeatures(G172.M7)) # We select the top 10 PCs for clustering and tSNE based on PCElbowPlot G172.M7 <- RunUMAP(G172.M7, reduction = "pca", dims = 1:25) G172.M7 <- FindNeighbors(G172.M7, reduction = "pca", dims = 1:25) G172.M7 <- FindClusters(G172.M7, resolution = 0.5 ) G172.M7.p1<- UMAPPlot(G172.M7, reduction = "umap", label=TRUE, label.size=5) G172.M7.p1 DefaultAssay(G172.M7) <- "RNA" G172.M7 <- NormalizeData(G172.M7, verbose = TRUE) d7 <- DotPlot(G172.M7, features = c(all_genes,'Cyp2b10','Cyp2d9'))+RotatedAxis() plot_grid(G172.M7.p1,d7, nrow=2) ``` ```{r} ########## function load_tissue_droplet############ droplet_metadata <- read.csv("/restricted/projectnb/waxmanlab/kkarri/scRNAseq_data_integration/metadata_droplet_liver.csv", sep=",", header = TRUE) colnames(droplet_metadata)[1] <- "channel" tissue_metadata = filter(droplet_metadata, tissue == tissue_of_interest)[,c('channel','tissue','subtissue','mouse.sex', 'mouse.id')] raw.data <- Read10X("/restricted/projectnb/waxmanlab/kkarri/scRNAseq_data_integration/Refined_cellmatrices/Liver-10X_P4_2/") colnames(raw.data) <- lapply(colnames(raw.data), function(x) paste0(tissue_metadata$channel[1],'_',x)) meta.data1 = data.frame(row.names = colnames(raw.data)) meta.data1['channel'] = tissue_metadata$channel[1] if (length(tissue_metadata$channel) > 1){ # Some tissues, like Thymus and Heart had only one channel for(i in 2:nrow(tissue_metadata)){ subfolder = paste0("/restricted/projectnb/waxmanlab/kkarri/scRNAseq_data_integration/Refined_cellmatrices/",tissue_of_interest, '-', tissue_metadata$channel[i]) new.data1 <- Read10X(data.dir = subfolder) colnames(new.data1) <- lapply(colnames(new.data1), function(x) paste0(tissue_metadata$channel[i],'_', x)) new.metadata1 = data.frame(row.names = colnames(new.data1)) new.metadata1['channel'] = tissue_metadata$channel[i] raw.data = cbind(raw.data, new.data1) meta.data1 = rbind(meta.data1, new.metadata1) } } rnames = row.names(meta.data1) meta.data1 <- merge(meta.data1, tissue_metadata, sort = F) row.names(meta.data1) <- rnames # Order the cells alphabetically to ensure consistency. ordered_cell_names = order(colnames(raw.data)) raw.data = raw.data[,ordered_cell_names] meta.data1 = meta.data1[ordered_cell_names,] # Find ERCC's, compute the percent ERCC, and drop them from the raw data. erccs <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = TRUE) percent.ercc <- Matrix::colSums(raw.data[erccs, ])/Matrix::colSums(raw.data) ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE) raw.data <- raw.data[-ercc.index,] # Create the Seurat object with all the data droplet <- CreateSeuratObject(raw.data) # dropseq droplet <- AddMetaData(object = droplet, meta.data1) droplet@meta.data$tech <- "droplet" #n.pcs = 10 #droplet <- SubsetData(droplet,subset.names = c("nGene", "nUMI"), low.thresholds = c(500, 1000)) # old version of seurat droplet <- subset(droplet, subset = nFeature_RNA > 500 & nCount_RNA > 1000) droplet <- NormalizeData(droplet, verbose = FALSE) droplet <- FindVariableFeatures(droplet, selection.method = "vst", nfeatures = 2000) droplet <- ScaleData(droplet, verbose = FALSE) #droplet <- RunPCA(droplet, npcs = 10, verbose = FALSE) droplet$stim <- "droplet" droplet$cond <- "ctrl" # droplet <- ScaleData(droplet, verbose = FALSE) droplet <- RunPCA(droplet, npcs = 30, verbose = FALSE) droplet <- RunUMAP(droplet, reduction = "pca", dims = 1:25) droplet <- FindNeighbors(droplet, reduction = "pca", dims = 1:10) droplet <- FindClusters(droplet, resolution = 0.5 ) p1<- UMAPPlot(droplet, reduction = "umap", group.by = "channel", label=TRUE, label.size=5) p2 <- UMAPPlot(droplet, label=TRUE, label.size=6) p3<- UMAPPlot(droplet, reduction = "umap", group.by = "mouse.sex", label=TRUE, label.size=5) res.used <- 1 #droplet <- FindClusters(object = droplet, reduction.type = "pca", dims.use = 1:n.pcs, resolution = res.used, print.output = 0, save.SNN = TRUE, force.recalc = TRUE) droplet <- RunTSNE(object = droplet, dims.use = 1:n.pcs, seed.use = 10, perplexity=30) TSNEPlot(object = droplet, do.label = T, pt.size = 1.2, label.size = 4) droplet <- RenameIdents(droplet, `0` = "Hep-Mid-M", `1` = "Hep-PC-F", `2` = "Hep-PP-F",`3` = "Hep-PP-M", `4` = "Hep-Mid-M", `5` = "Hep-PC-M", `6` = "Hep-Mid-F", `7` = "Hep-Mid-F", `8` = "Endo-F", `9` = "Bileduct-F") ### this is tranformation option for develpment SCtransform program ####### droplet <- SCTransform(droplet,verbose =TRUE) ######3 Smartseq ######################### plate_metadata <- read.csv("/restricted/projectnb/waxmanlab/kkarri/scRNAseq_data_integration/Liver_facs_annotation.csv", sep=",", header = TRUE) colnames(plate_metadata)[1] <- "plate.barcode" raw.data = read.csv("/restricted/projectnb/waxmanlab/kkarri/scRNAseq_data_integration/liver_facs_scrna_data.csv", sep=",", row.names=1) colnames(plate_metadata)[1] <- "plate.barcode" plate.barcodes = lapply(colnames(raw.data), function(x) strsplit(strsplit(x, "_")[[1]][1], '.', fixed=TRUE)[[1]][2]) barcode.df = t.data.frame(as.data.frame(plate.barcodes)) rownames(barcode.df) = colnames(raw.data) barcode.df= cbind(barcode.df, colnames(raw.data)) colnames(barcode.df) = c('plate.barcode1', 'plate.barcode') rnames = row.names(barcode.df) meta.data <- merge(barcode.df, plate_metadata, by='plate.barcode', sort = F) row.names(meta.data) <- rnames # Sort cells by cell name meta.data = meta.data[order(rownames(meta.data)), ] raw.data = raw.data[,rownames(meta.data)] # Create the Seurat object with all the data smartseq <- CreateSeuratObject(raw.data) smartseq <- AddMetaData(object = smartseq, meta.data) #smartseq@meta.data$tech <- "smartseq" smartseq$stim <- "smartseq" smartseq$cond <- "ctrl" #smartseq <- SubsetData(smartseq,subset.names = c("nGene", "nUMI"),low.thresholds = c(500, 1000)) #old version of seurat smartseq<- subset(smartseq, subset = nFeature_RNA > 500 & nCount_RNA > 1000) #smartseq <- SCTransform(smartseq) smartseq <- NormalizeData(smartseq, verbose = FALSE) smartseq <- FindVariableFeatures(smartseq, selection.method = "vst", nfeatures = 2000) smartseq <- ScaleData(smartseq, verbose = FALSE) smartseq <- RunPCA(smartseq, npcs = 30, verbose = FALSE) smartseq <- RunUMAP(smartseq, reduction = "pca", dims = 1:25) smartseq <- FindNeighbors(smartseq, reduction = "pca", dims = 1:25) smartseq <- FindClusters(smartseq, resolution = 0.5 ) #p4<- UMAPPlot(smartseq, reduction = "umap", group.by = "channel", label=TRUE, label.size=5) p5 <- UMAPPlot(smartseq, label=TRUE, label.size=6) p6<- UMAPPlot(smartseq, reduction = "umap", group.by = "mouse.sex", label=TRUE, label.size=5) ########## sctransform ###################3 smartseq <- SCTransform(smartseq) ############################### Integration Regular workflow ############################ anchors <- FindIntegrationAnchors(object.list = c(droplet.list, smartseq.list,G171B.list, G172.M1.list, G172.M2.list), dims = 1:50, anchor.features = 3000) combined <- IntegrateData(anchorset = anchors, dims = 1:50) DefaultAssay(combined) <- "integrated" # Run the standard workflow for visualization and clustering combined <- ScaleData(combined, verbose = FALSE) combined <- RunPCA(combined, npcs = 30, verbose = FALSE) # t-SNE and Clustering combined <- RunUMAP(combined, reduction = "pca", dims = 1:20) combined <- FindNeighbors(combined, reduction = "pca", dims = 1:20) combined <- FindClusters(combined, resolution = 0.5 ) #combined <- RunTSNE(combined, reduction = "pca", dims = 1:20) # Visualization p1 <- DimPlot(combined, reduction = "umap", group.by = "stim") p2 <- DimPlot(combined, reduction = "umap", group.by = "mouse.sex") p3 <- DimPlot(combined, reduction = "umap", label = TRUE) p4 <- UMAPPlot(combined, label=TRUE) plot_grid(p2, p3,p4) DimPlot(combined, reduction = "umap", split.by = "stim") p5 <- DimPlot(combined, reduction = "tsne", group.by = "stim") p6 <- DimPlot(combined, reduction = "tsne", group.by = "mouse.sex") p7 <- DimPlot(combined, reduction = "tsne", label = TRUE) p8 <- TSNEPlot(combined, label =T) plot_grid(p6, p7,p8) DimPlot(combined, reduction = "tsne", split.by = "stim") #################################### Refernce based Integration ################################ droplet.list <- SplitObject(droplet, split.by = "mouse.sex") smartseq.list <- SplitObject(smartseq, split.by = "stim") G172.M1.list <- SplitObject(G172.M1, split.by = "stim") G172.M2.list <- SplitObject(G172.M2, split.by = "stim") G172.M6.list <- SplitObject(G172.M6, split.by = "stim") G171B.list <- SplitObject(G171B, split.by="stim") ctrl.list <- c(droplet.list, smartseq.list) for (i in names(ctrl.list)) { ctrl.list[[i]] <- SCTransform(ctrl.list[[i]], verbose =TRUE) } ctrl.list1 <- c(ctrl.list, G171B.list) #dims = 1:50 ref.features <- SelectIntegrationFeatures(object.list = ctrl.list1, dims=1:50, anchor.features = 3000) ref.list <- PrepSCTIntegration(object.list = ctrl.list1, anchor.features = ref.features) ref.anchors <- FindIntegrationAnchors(object.list = ref.list, normalization.method = "SCT", anchor.features = ref.features, reference = c(1,2,3)) ref.integrated <- IntegrateData(anchorset = ref.anchors, normalization.method = "SCT") DefaultAssay(ref.integrated) <- "integrated" ref.integrated <- ScaleData(ref.integrated, verbose = FALSE) ref.integrated <- RunPCA(object = ref.integrated, npcs=30,verbose = FALSE) ref.integrated <- RunUMAP(object = ref.integrated ,dims = 1:30, seed.use = 10, perplexity=30) ref.integrated <- FindNeighbors(ref.integrated, reduction = "pca", dims = 1:30) ref.integrated <- FindClusters(ref.integrated, resolution = 0.2 ) r1 <- DimPlot(ref.integrated, split.by="stim", pt.size = 0.5) r2 <- DimPlot(ref.integrated, label=TRUE, pt.size = 0.5) r3 <- DimPlot(ref.integrated, split.by = c("cond"), pt.size = 0.5) plots <- lapply(X = plots, FUN = function(x) x + theme(legend.position = "top") + guides(color = guide_legend(nrow = 4, byrow = TRUE, override.aes = list(size = 2.5)))) CombinePlots(plots) ref.integrated$celltype.stim <- paste(Idents(ref.integrated), ref.integrated$stim, sep = "_") ref.integrated$seurat_clusters <- Idents(ref.integrated) Idents(ref.integrated) <- "celltype.stim" fp <- FeaturePlot(ref.integrated, features = "ncRNA-as-chr10-8460", cols = c("lightgrey","darkred"), order = TRUE, shape.by = "stim", pt.size = 2) DefaultAssay(ref.integrated) <-"RNA" ref.integrated <- NormalizeData(ref.integrated) ref.integrated <- ScaleData(ref.integrated) dot1 <- DotPlot(ref.integrated, features= all_genes)+RotatedAxis() d1 <- DoHeatmap(ref.integrated, features = c(all_genes,"Mup20","Xist"), group.by = "seurat_clusters", assay= 'RNA') #d1 <- DoHeatmap(ref.integrated, features = highTPM, group.by = "celltype.stim", assay= 'RNA',raster = F, disp.max = 0.5)+scale_fill_gradientn(colors = (RColorBrewer::brewer.pal(n = 9, name = "PuRd")) ) + guides(color=FALSE) Drop.combined.markers <- FindAllMarkers(object = ref.integrated, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25) ############ merge G172 M1 #############33 droplet.list <- SplitObject(droplet, split.by = "mouse.sex") smartseq.list <- SplitObject(smartseq, split.by = "stim") G172.M1.list <- SplitObject(G172.M1, split.by = "stim") G172.M2.list <- SplitObject(G172.M2, split.by = "stim") G172.M6.list <- SplitObject(G172.M6, split.by = "stim") G171B.list <- SplitObject(G171B, split.by="stim") ctrl.list.2 <- c( G172.M1.list, G172.M2.list) for (i in names(ctrl.list.2)) { ctrl.list.2[[i]] <- SCTransform(ctrl.list.2[[i]], verbose =TRUE) } ref.features.1 <- SelectIntegrationFeatures(object.list = ctrl.list.2, dims = 1:50, anchor.features = 3000) ref.list.1 <- PrepSCTIntegration(object.list = ctrl.list.2, anchor.features = ref.features.1) ref.anchors.1 <- FindIntegrationAnchors(object.list = ref.list.1, normalization.method = "SCT", anchor.features = ref.features.1, reference = c(1,2)) ref.integrated.1 <- IntegrateData(anchorset = ref.anchors.1, normalization.method = "SCT") DefaultAssay(ref.integrated.1) <- "integrated" ref.integrated.1 <- ScaleData(ref.integrated.1, verbose = FALSE) ref.integrated.1 <- RunPCA(object = ref.integrated.1, npcs=30,verbose = FALSE) ref.integrated.1 <- RunUMAP(object = ref.integrated.1, dims = 1:30, seed.use = 10 , perplexity=30) #ref.integrated.1 <- RunTSNE(object = ref.integrated.1, dims.use = 1:n.pcs, seed.use = 10, perplexity=30) ref.integrated.1 <- FindNeighbors(ref.integrated.1, reduction = "pca", dims = 1:30) ref.integrated.1 <- FindClusters(ref.integrated.1, resolution = 0.2) rG172M1.1 <- UMAPPlot(ref.integrated.1, split.by="stim", label=T) rG172M1.2 <- UMAPPlot(ref.integrated.1, label=TRUE, combine = FALSE) rG172M1.3 <- UMAPPlot(ref.integrated.1, split.by = c("mouse.sex")) rG172M1.4 <- TSNEPlot(ref.integrated.1, split.by="stim") DefaultAssay(ref.integrated.1) <-"RNA" ref.integrated.1 <- NormalizeData(ref.integrated.1) ref.integrated.1 <- ScaleData(ref.integrated.1) DoHeatmap(ref.integrated.1, features = c(all_genes,'Sox9','Mup20'), group.by = "seurat_clusters", assay= 'RNA' ,raster = F)+scale_fill_gradientn(colors = (RColorBrewer::brewer.pal(n = 9, name = "PuRd")) ) + guides(color=FALSE) ref.integrated.1$celltype <- RenameIdents(ref.integrated.1, c(`0` = "PP", `1` = "PC", `2` = "Endo",`3` = "HSC", `4` = "Kupffer", `5` = "Dividing", `6` = "Immune", `7` = "B-NK", `8` = "NA")) ref.integrated.1.markers <- FindAllMarkers(ref.integrated.1, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25) ref.integrated.1.markers %>% group_by(cluster) %>% top_n(n = 2, wt = avg_logFC) write.csv(ref.integrated.1.markers, "ref_integrated_G172M1-M2_Allmarker") ref.integrated.1$celltype.stim <- paste(Idents(ref.integrated.1), ref.integrated.1$stim, sep = "_") ref.integrated.1$celltype <- Idents(ref.integrated.1) Idents(ref.integrated.1) <- "celltype.stim" DoHeatmap(ref.integrated.1, features = TPM4, group.by = "seurat_clusters", assay= 'RNA' ) ref.integrated.1.markers <- FindMarkers(ref.integrated.1, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25) ref.integrated.1.markers %>% group_by(cluster) %>% top_n(n = 2, wt = avg_logFC) ``` G172M1 and G172 M2 DE analysis ```{r} DE1.1 <- FindMarkers(ref.integrated.1, ident.1 = c("0_G172M1" ,"1_G172M1", "2_G172M1" ,"3_G172M1" ,"4_G172M1" ,"5_G172M1" ,"6_G172M1","7_G172M1", "8_G172M1"), ident.2 = c("0_G172M2" ,"1_G172M2", "2_G172M2" ,"3_G172M2" ,"4_G172M2" ,"5_G172M2" ,"6_G172M2","7_G172M2", "8_G172M2"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DE_All_M1_M2 <- DE1.1 DE_hep_NPC <- FindMarkers(ref.integrated.1, ident.1 = c("0_G172M2" ,"1_G172M2"), ident.2 = c("2_G172M2" ,"3_G172M2" ,"4_G172M2" ,"5_G172M2" ,"6_G172M2","7_G172M2"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DE_NPC_M1_M2 <- FindMarkers(ref.integrated.1, ident.1 = c( "2_G172M1" ,"3_G172M1" ,"4_G172M1" ,"5_G172M1" ,"6_G172M1","7_G172M1"), ident.2 = c("2_G172M2" ,"3_G172M2" ,"4_G172M2" ,"5_G172M2" ,"6_G172M2","7_G172M2"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DE_Drop <- FindAllMarkers(ref.integrated, features = ) DE0.1 <- FindMarkers(ref.integrated.1, ident.1 = c("0_G172M1"), ident.2 = c("0_G172M2"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) write.csv(DE0.1, "marker/DE0.1_G172_0_M1_M2") DE1.1 <- FindMarkers(ref.integrated.1, ident.1 = c("1_G172M1"), ident.2 = c("1_G172M2"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DE2.1 <- FindMarkers(ref.integrated.1, ident.1 = c("2_G172M1"), ident.2 = c("2_G172M2"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DE3.1 <- FindMarkers(ref.integrated.1, ident.1 = c("3_G172M1"), ident.2 = c("3_G172M2"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DE4.1 <- FindMarkers(ref.integrated.1, ident.1 = c("4_G172M1"), ident.2 = c("4_G172M2"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DE5.1 <- FindMarkers(ref.integrated.1, ident.1 = c("5_G172M1"), ident.2 = c("5_G172M2"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DE6.1 <- FindMarkers(ref.integrated.1, ident.1 = c("6_G172M1"), ident.2 = c("6_G172M2"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DE7.1 <- FindMarkers(ref.integrated.1, ident.1 = c("7_G172M1"), ident.2 = c("7_G172M2"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) ``` ```{r} ################# G171 TCPO expsosed ######## G171B_metadata_G171B <- read.csv("/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G171/Analysis/G171_metadata_droplet_liver.csv", sep=",", header = TRUE) colnames(G171B_metadata_G171B)[1] <- "channel" tissue_metadata_G171B = filter(G171B_metadata_G171B, tissue == tissue_of_interest)[,c('channel','tissue','subtissue','mouse.sex', 'mouse.id')] raw.data <- Read10X("/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G171/Analysis/Transcript_Refined/Liver-10X_G171B/") colnames(raw.data) <- lapply(colnames(raw.data), function(x) paste0(tissue_metadata_G171B$channel[1],'_',x)) meta.data1 = data.frame(row.names = colnames(raw.data)) meta.data1['channel'] = tissue_metadata_G171B$channel[1] rnames = row.names(meta.data1) meta.data1 <- merge(meta.data1, tissue_metadata_G171B, sort = F) row.names(meta.data1) <- rnames # Order the cells alphabetically to ensure consistency. ordered_cell_names = order(colnames(raw.data)) raw.data = raw.data[,ordered_cell_names] meta.data1 = meta.data1[ordered_cell_names,] # Find ERCC's, compute the percent ERCC, and drop them from the raw data. erccs <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = TRUE) percent.ercc <- Matrix::colSums(raw.data[erccs, ])/Matrix::colSums(raw.data) ercc.index <- grep(pattern = "^ERCC-", x = rownames(x = raw.data), value = FALSE) raw.data <- raw.data[-ercc.index,] ncRNA.genes <- grep(pattern = "^ncRNA", x = rownames(x = raw.data), value = TRUE) percent.ncRNA <- Matrix::colSums(raw.data[ncRNA.genes, ])/Matrix::colSums(raw.data) KRAB.genes <- grep(pattern = "^KRAB", x = rownames(x = raw.data), value = TRUE) cherry.genes <- grep(pattern = "^mcherry", x = rownames(x = raw.data), value = TRUE) # Create the Seurat object with all the data G171B <- CreateSeuratObject(raw.data) # dropseq G171B <- AddMetaData(object = G171B, meta.data1) G171B@meta.data$tech <- "G171B" #G171B <- SubsetData(G171B,subset.names = c("nGene", "nUMI"), low.thresholds = c(500, 1000)) # old version of seurat #G171B <- subset(G171B, subset = nFeature_RNA > 500 & nCount_RNA > 1000) G171B <- NormalizeData(G171B, verbose = FALSE) G171B <- FindVariableFeatures(G171B, selection.method = "vst", nfeatures = 2000) G171B$stim <- "G171B" G171B$cond <- "TCPO" ### this is tranformation option for develpment SCtransform program ####### G171B <- SCTransform(G171B,verbose =TRUE) ``` diffusion plt code ```{r} # Before running MDS, we first calculate a distance matrix between all pairs of cells. Here we # use a simple euclidean distance metric on all genes, using scale.data as input d <- dist(t(GetAssayData(combined, slot = "scale.data"))) # Run the MDS procedure, k determines the number of dimensions mds <- cmdscale(d = d, k = 2) # cmdscale returns the cell embeddings, we first label the columns to ensure downstream # consistency colnames(mds) <- paste0("MDS_", 1:2) # We will now store this as a custom dimensional reduction called 'mds' combined[["mds"]] <- CreateDimReducObject(embeddings = mds, key = "MDS_", assay = DefaultAssay(combined)) # We can now use this as you would any other dimensional reduction in all downstream functions DimPlot(combined, reduction = "mds", pt.size = 0.5) ``` ```{r} genes_hep_main =c('Alb', 'Ttr', 'Apoa1', 'Serpina1c') genes_hep = c('Alb', 'Ttr', 'Apoa1', 'Serpina1c', 'Cyp2e1', 'Glul', 'Oat', 'Gulo', 'Ass1', 'Hamp', 'Gstp1', 'Ubb', 'Cyp2f2', 'Pck1', 'Hal', 'Cdh1') genes_endo = c('Pecam1', 'Nrp1', 'Kdr','Oit3') genes_kuppfer = c( 'Clec4f', 'Cd68') genes_nk = c('Il2rb', 'Nkg7', 'Cxcr6', 'Gzma') genes_b = c('Cd79a', 'Cd79b') genes_bec = c('Epcam', 'Krt19', 'Krt7') genes_immune = 'Ptprc' HSC = c("Dcn","Lama1","Nes") Dividing = "Top2a" Bplasma= "Jchain" Mac= "Csf1r" Chol="Sox9" sex <- c("Cyp2d9", "ncRNA-inter_chrX-15394") all_genes = c(genes_hep, genes_endo, genes_kuppfer, Mac,genes_nk, genes_b, genes_bec, genes_immune, HSC, Dividing) genes_bec_b_immune = c(genes_bec,genes_b,genes_immune) genes_zones = c('Cyp2e1', 'Glul', 'Oat', 'Gulo', 'Ass1', 'Hamp', 'Gstp1', 'Ubb', 'Cyp2f2', 'Pck1', 'Hal', 'Cdh1') receptor_KO <- c("ncRNA-inter-chr7-5998","Cyp2b10","Nr1i2","Nr1i3","Ppara","Pparg","Ppargc1b","Ppard") cell <- c("Stab2","Csf1r","Cd3g","Ebf1","Irf8","Sox9","Apoc3","Top2a","Dcn") ``` ```{r} TPM4<- c('ncRNA-inter-chr7-6524', 'ncRNA-inter-chr19-14853', 'ncRNA-inter-chr6-5675', 'ncRNA-inter-chr4-3468', 'ncRNA-inter-chr9-8122', 'ncRNA-inter-chr12-10476', 'ncRNA-inter-chr17-14026', 'ncRNA-as-chr2-1457', 'ncRNA-as-chr19-14883', 'ncRNA-as-chr10-8460', 'ncRNA-as-chr5-4325', 'ncRNA-inter-chr7-5998', 'ncRNA-as-chr9-7843', 'ncRNA-as-chr9-8142', 'ncRNA-inter-chr11-9925', 'ncRNA-inter-chr19-14873', 'ncRNA-as-chr9-8172', 'ncRNA-inter-chr4-3779', 'ncRNA-inter-chr3-2504', 'ncRNA-as-chr19-15054', 'ncRNA-inter-chr8-7423', 'ncRNA-as-chr7-6302', 'ncRNA-inter-chr10-9418', 'ncRNA-inter-chr12-10454', 'ncRNA-as-chr7-5999', 'ncRNA-as-chr6-5335', 'ncRNA-inter-chr19-14987', 'ncRNA-inter-chr16-13170', 'ncRNA-inter-chr3-2988', 'ncRNA-inter-chr8-7430', 'ncRNA-inter-chr3-2168', 'ncRNA-inter-chr9-7874', 'ncRNA-inter-chr4-3778', 'ncRNA-inter-chr2-2011', 'ncRNA-inter-chr5-4335', 'ncRNA-inter-chr9-8301', 'ncRNA-inter-chr16-13510', 'ncRNA-as-chr9-8401', 'ncRNA-as-chr16-13512', 'ncRNA-as-chr12-10896', 'ncRNA-as-chr8-7359', 'ncRNA-as-chr5-4744', 'ncRNA-inter-chr5-4499', 'ncRNA-inter-chr16-13509', 'ncRNA-inter-chr17-13692', 'ncRNA-as-chr17-13834', 'ncRNA-inter-chr9-8147', 'ncRNA-inter-chr10-8697', 'ncRNA-inter-chr6-5551', 'ncRNA-as-chr9-8317', 'ncRNA-inter-chr7-6509', 'ncRNA-as-chr19-14977', 'ncRNA-inter-chr6-5318', 'ncRNA-inter-chr5-4578', 'ncRNA-inter-chr12-10509', 'ncRNA-as-chr10-9015', 'ncRNA-inter-chr3-2411', 'ncRNA-inter-chr9-7875', 'ncRNA-inter-chr5-4336', 'ncRNA-inter-chr12-10910', 'ncRNA-as-chr1-782', 'ncRNA-inter-chr17-13924', 'ncRNA-intra-chr7-5920', 'ncRNA-inter-chr16-13225', 'ncRNA-inter-chr3-2269', 'ncRNA-inter-chr14-12016', 'ncRNA-as-chr4-3800', 'ncRNA-as-chr5-4655', 'ncRNA-inter-chr9-7989', 'ncRNA-intra-chr5-4728', 'ncRNA-inter-chrX-15248', 'ncRNA-inter-chr10-8767', 'ncRNA-inter-chr19-14717', 'ncRNA-inter-chr8-6766', 'ncRNA-inter-chr13-11122', 'ncRNA-inter-chr7-6074', 'ncRNA-inter-chr15-12439', 'ncRNA-as-chr11-9787', 'ncRNA-inter-chr2-1827', 'ncRNA-as-chr19-14976', 'ncRNA-inter-chr19-14947', 'ncRNA-inter-chr6-5248', 'ncRNA-inter-chr2-1098', 'ncRNA-inter-chr8-7180', 'ncRNA-inter-chr9-8118', 'ncRNA-inter-chr14-12199', 'ncRNA-as-chr6-5336', 'ncRNA-inter-chr4-3867', 'ncRNA-inter-chr10-9000', 'ncRNA-inter-chr14-12290', 'ncRNA-inter-chr2-1491', 'ncRNA-inter-chr15-12606', 'ncRNA-inter-chr10-9222', 'ncRNA-inter-chr5-4322', 'ncRNA-inter-chr12-10942', 'ncRNA-inter-chr18-14690', 'ncRNA-inter-chr2-1471', 'ncRNA-inter-chr3-2410', 'ncRNA-inter-chr19-14880', 'ncRNA-inter-chr13-11074', 'ncRNA-inter-chr9-8056', 'ncRNA-inter-chr5-4654', 'ncRNA-inter-chr3-2166', 'ncRNA-inter-chr8-6944', 'ncRNA-as-chr7-6065', 'ncRNA-inter-chr8-6896', 'ncRNA-inter-chr2-1963', 'ncRNA-inter-chr4-3425', 'ncRNA-inter-chr13-11385', 'ncRNA-inter-chr9-7885', 'ncRNA-as-chr10-9411', 'ncRNA-as-chr9-8419', 'ncRNA-as-chr12-10618', 'ncRNA-inter-chr7-6390', 'ncRNA-inter-chr17-14151', 'ncRNA-as-chr13-11787', 'ncRNA-as-chr2-1965', 'ncRNA-inter-chr6-5721', 'ncRNA-inter-chr6-5822', 'ncRNA-inter-chr11-9635', 'ncRNA-inter-chr11-9965', 'ncRNA-inter-chr4-3052', 'ncRNA-inter-chr17-13857', 'ncRNA-inter-chr13-11201', 'ncRNA-inter-chr6-5249', 'ncRNA-inter-chr19-14851', 'ncRNA-inter-chr6-5638', 'ncRNA-inter-chr17-14130', 'ncRNA-inter-chr9-7993', 'ncRNA-inter-chr18-14656', 'ncRNA-inter-chr9-7992', 'ncRNA-inter-chr1-566', 'ncRNA-inter-chr12-10715', 'ncRNA-inter-chr17-14102', 'ncRNA-inter-chr4-3010', 'ncRNA-inter-chr3-2764', 'ncRNA-inter-chr4-3306', 'ncRNA-inter-chr19-14979', 'ncRNA-inter-chr1-570', 'ncRNA-inter-chr19-14790', 'ncRNA-inter-chr4-3282', 'ncRNA-inter-chr5-4777', 'ncRNA-inter-chr8-7605', 'ncRNA-inter-chr9-8000', 'ncRNA-as-chr15-12920', 'ncRNA-as-chr1-369', 'ncRNA-inter-chr19-14952', 'ncRNA-as-chr9-8393', 'ncRNA-as-chr14-12074', 'ncRNA-inter-chr12-10415', 'ncRNA-inter-chr7-6559', 'ncRNA-inter-chr1-630', 'ncRNA-inter-chr4-3142', 'ncRNA-inter-chr6-5131', 'ncRNA-inter-chr16-13050', 'ncRNA-inter-chr7-6411', 'ncRNA-inter-chr5-4746', 'ncRNA-inter-chr1-633', 'ncRNA-inter-chr10-8461', 'ncRNA-inter-chr2-2016', 'ncRNA-inter-chr6-5137', 'ncRNA-as-chr4-3300', 'ncRNA-inter-chr4-3009', 'ncRNA-inter-chr2-1923', 'ncRNA-inter-chr1-670', 'ncRNA-inter-chr15-12835', 'ncRNA-inter-chr18-14655', 'ncRNA-inter-chr16-13211', 'ncRNA-as-chr7-6050', 'ncRNA-inter-chr7-6508', 'ncRNA-inter-chr11-10206', 'ncRNA-inter-chr13-11399', 'ncRNA-inter-chr8-6757', 'ncRNA-inter-chr1-129', 'ncRNA-inter-chr12-10459', 'ncRNA-inter-chr7-6709', 'ncRNA-inter-chr12-10713', 'ncRNA-inter-chr7-6523', 'ncRNA-inter-chr2-2017', 'ncRNA-inter-chr7-6343', 'ncRNA-inter-chr1-63', 'ncRNA-as-chr3-2936', 'ncRNA-inter-chr18-14691', 'ncRNA-inter-chr7-6097', 'ncRNA-inter-chr6-5723') ``` ```{r} DefaultAssay(droplet) <- "RNA" #droplet <- NormalizeData(combined, verbose = TRUE, normalization.method = "RC", scale.factor = 1e6) combined <- NormalizeData(combined, verbose = TRUE) DotPlot(combined, features = all_genes) FeaturePlot(combined, features = genes_hep_main, min.cutoff = "q9") #hepatocytes subtissplot <- DotPlot(combined, features = c(genes_hep_main, genes_endo, genes_bec_b_immune, genes_kuppfer, genes_nk)) PC <- DotPlot(combined, features = c(genes_hep_main,genes_zones)) NPC <- DotPlot(combined, features = c(genes_endo,genes_kuppfer, genes_nk)) all <- DotPlot(combined, features=c(all_genes)) ### coexpression plots#### f1 <- FeaturePlot(KO.cells, features = c('Cyp2b10','ncRNA-inter-chr7-5998'), reduction = "mds", order = TRUE,split.by = "stim", blend = TRUE,sort.cell = TRUE, max.cutoff = 0.5) ########## this is exact averaging formula ###############33 x <- (AverageExpression(KO.cells, verbose = TRUE, assays = "RNA" ,slot="counts")$RNA) x["ncRNA-inter-chr7-5998",] # G171B G171C #ncRNA-inter-chr7-5998 1.871795 1.091463 ######## #Idents(combined) <- factor(Idents(combined), levels = c(0,1,12)) markers.to.plot <- c("Alb","ncRNA-inter-chr7-5998") DotPlot(combined, features = rev(markers.to.plot), cols = c("blue", "red"), dot.scale = 8, split.by = "stim") + RotatedAxis() FeaturePlot(combined, features = c("Alb", "ncRNA-inter-chr7-5998","Cyp2b10","dSaCas9","KRAB","AAV8-mCherry"), split.by = "stim", max.cutoff = 3, cols = c("grey", "red")) ######################### vlnplot ########################## plots <- VlnPlot(combined, features = c("Alb", "ncRNA-inter-chr7-5998","Cyp2b10","dSaCas9"), split.by = "stim", group.by = "seurat_clusters", pt.size = 0, combine = FALSE) CombinePlots(plots = plots, ncol = 1) plots <- VlnPlot(combined, features = c("Lhx4","Dtna","Fam189a1","Galnt16","Kalrn"), split.by = "stim", group.by = "seurat_clusters", pt.size = 0, combine = FALSE) CombinePlots(plots = plots, ncol = 1) #endothelial DotPlot(combined, features = genes_endo) #zones zones <- DotPlot(combined, features = genes_zones) f1 <- FeaturePlot(combined, features = c('Cyp2e1','Cyp2f2','Ass1'), min.cutoff = "q9", reduction = "tsne") DimPlot(combined, label = TRUE) save(combined, file="Seurat_smart-drop_integrated.Robj") ################# save raw counts from cluster ##################### Idents(combined) <- "stim" ### to avergae out the matrix from KO cells combined.raw.data.0.1 <- as.matrix(GetAssayData(combined, slot = "data")[, WhichCells(combined, ident = 0,idents = "stim")]) combined.raw.data.1 <- as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 0)]) combined.raw.data.2 <- as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 0)]) #combined.raw.data.[i] <- as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 1)]) #combined.raw.data.12 <-as.matrix(GetAssayData(combined, slot = "counts")[, WhichCells(combined, ident = 12)]) #combined.raw.data.1 <- as.matrix(GetAssayData(combined, slot = "counts")) x <- AverageExpression(test.combined,assays = "RNA",add.ident = "stim", slot = "data",use.scale = FALSE, use.counts = FALSE)$RNA #}######## CAR data avg.combined.cells <- (AverageExpression(combined, verbose = FALSE)$RNA) avg.combined.cells$gene <- rownames(avg.combined.cells) CAR_FP <- FeaturePlot(combined, features = c('Cyp2b10','Nr1i3'), reduction = "umap", order = TRUE,split.by = "stim", blend = TRUE,sort.cell = TRUE, max.cutoff = 1, min.cutoff = 0, pt.size = 0.5, repel = TRUE) CAR_DOT_NR <- DotPlot(combined, features = 'Nr1i3', col.min = 0) Cyp2b10_FP <- FeaturePlot(combined, features = 'Cyp2b10', reduction = "umap", min.cutoff = 0) ########### tSNE ################################# combined <- NormalizeData(object = combined) combined <- FindVariableFeatures(combined, selection.method = "vst", nfeatures = 2000) ``` ```{r} G172_TPM_count <- ref.integrated.1 G172_TPM_count <- NormalizeData(G172_TPM_count,assay = "RNA", normalization.method = "RC", scale.factor = 1e6) TPMcount0M1<- cbind(as.matrix(GetAssayData(G172_TPM_count, slot = "counts")[, WhichCells(G172_TPM_count, ident = '0_G172M1')]), as.matrix(GetAssayData(G172_TPM_count, slot = "data")[, WhichCells(G172_TPM_count, ident = '0_G172M1')])) TPMcount1M1<- cbind(as.matrix(GetAssayData(G172_TPM_count, slot = "counts")[, WhichCells(G172_TPM_count, ident = '1_G172M1')]), as.matrix(GetAssayData(G172_TPM_count, slot = "data")[, WhichCells(G172_TPM_count, ident = '1_G172M1')])) TPMcount2M1<- cbind(as.matrix(GetAssayData(G172_TPM_count, slot = "counts")[, WhichCells(G172_TPM_count, ident = '2_G172M1')]), as.matrix(GetAssayData(G172_TPM_count, slot = "data")[, WhichCells(G172_TPM_count, ident = '2_G172M1')])) TPMcount3M1<- cbind(as.matrix(GetAssayData(G172_TPM_count, slot = "counts")[, WhichCells(G172_TPM_count, ident = '3_G172M1')]), as.matrix(GetAssayData(G172_TPM_count, slot = "data")[, WhichCells(G172_TPM_count, ident = '3_G172M1')])) TPMcount4M1<- cbind(as.matrix(GetAssayData(G172_TPM_count, slot = "counts")[, WhichCells(G172_TPM_count, ident = '4_G172M1')]), as.matrix(GetAssayData(G172_TPM_count, slot = "data")[, WhichCells(G172_TPM_count, ident = '4_G172M1')])) TPMcount5M1<- cbind(as.matrix(GetAssayData(G172_TPM_count, slot = "counts")[, WhichCells(G172_TPM_count, ident = '5_G172M1')]), as.matrix(GetAssayData(G172_TPM_count, slot = "data")[, WhichCells(G172_TPM_count, ident = '5_G172M1')])) TPMcount6M1<- cbind(as.matrix(GetAssayData(G172_TPM_count, slot = "counts")[, WhichCells(G172_TPM_count, ident = '6_G172M1')]), as.matrix(GetAssayData(G172_TPM_count, slot = "data")[, WhichCells(G172_TPM_count, ident = '6_G172M1')])) TPMcount7M1<- cbind(as.matrix(GetAssayData(G172_TPM_count, slot = "counts")[, WhichCells(G172_TPM_count, ident = '7_G172M1')]), as.matrix(GetAssayData(G172_TPM_count, slot = "data")[, WhichCells(G172_TPM_count, ident = '7_G172M1')])) TPMcount8M1<- cbind(as.matrix(GetAssayData(G172_TPM_count, slot = "counts")[, WhichCells(G172_TPM_count, ident = '8_G172M1')]), as.matrix(GetAssayData(G172_TPM_count, slot = "data")[, WhichCells(G172_TPM_count, ident = '8_G172M1')])) TPMcount0M2<- cbind(as.matrix(GetAssayData(G172_TPM_count, slot = "counts")[, WhichCells(G172_TPM_count, ident = '0_G172M2')]), as.matrix(GetAssayData(G172_TPM_count, slot = "data")[, WhichCells(G172_TPM_count, ident = '0_G172M2')])) TPMcount1M2<- cbind(as.matrix(GetAssayData(G172_TPM_count, slot = "counts")[, WhichCells(G172_TPM_count, ident = '1_G172M2')]), as.matrix(GetAssayData(G172_TPM_count, slot = "data")[, WhichCells(G172_TPM_count, ident = '1_G172M2')])) TPMcount2M2<- cbind(as.matrix(GetAssayData(G172_TPM_count, slot = "counts")[, WhichCells(G172_TPM_count, ident = '2_G172M2')]), as.matrix(GetAssayData(G172_TPM_count, slot = "data")[, WhichCells(G172_TPM_count, ident = '2_G172M2')])) TPMcount3M2<- cbind(as.matrix(GetAssayData(G172_TPM_count, slot = "counts")[, WhichCells(G172_TPM_count, ident = '3_G172M2')]), as.matrix(GetAssayData(G172_TPM_count, slot = "data")[, WhichCells(G172_TPM_count, ident = '3_G172M2')])) TPMcount4M2<- cbind(as.matrix(GetAssayData(G172_TPM_count, slot = "counts")[, WhichCells(G172_TPM_count, ident = '4_G172M2')]), as.matrix(GetAssayData(G172_TPM_count, slot = "data")[, WhichCells(G172_TPM_count, ident = '4_G172M2')])) TPMcount5M2<- cbind(as.matrix(GetAssayData(G172_TPM_count, slot = "counts")[, WhichCells(G172_TPM_count, ident = '5_G172M2')]), as.matrix(GetAssayData(G172_TPM_count, slot = "data")[, WhichCells(G172_TPM_count, ident = '5_G172M2')])) TPMcount6M2<- cbind(as.matrix(GetAssayData(G172_TPM_count, slot = "counts")[, WhichCells(G172_TPM_count, ident = '6_G172M2')]), as.matrix(GetAssayData(G172_TPM_count, slot = "data")[, WhichCells(G172_TPM_count, ident = '6_G172M2')])) TPMcount7M2<- cbind(as.matrix(GetAssayData(G172_TPM_count, slot = "counts")[, WhichCells(G172_TPM_count, ident = '7_G172M2')]), as.matrix(GetAssayData(G172_TPM_count, slot = "data")[, WhichCells(G172_TPM_count, ident = '7_G172M2')])) TPMcount8M2<- cbind(as.matrix(GetAssayData(G172_TPM_count, slot = "counts")[, WhichCells(G172_TPM_count, ident = '8_G172M2')]), as.matrix(GetAssayData(G172_TPM_count, slot = "data")[, WhichCells(G172_TPM_count, ident = '8_G172M2')])) write.csv(TPMcount0M1, "CountResult/counts_TPMcount0M1") write.csv(TPMcount1M1, "CountResult/counts_TPMcount1M1") write.csv(TPMcount2M1, "CountResult/counts_TPMcount2M1") write.csv(TPMcount3M1, "CountResult/counts_TPMcount3M1") write.csv(TPMcount4M1, "CountResult/counts_TPMcount4M1") write.csv(TPMcount5M1, "CountResult/counts_TPMcount5M1") write.csv(TPMcount6M1, "CountResult/counts_TPMcount6M1") write.csv(TPMcount7M1, "CountResult/counts_TPMcount7M1") write.csv(TPMcount8M1, "CountResult/counts_TPMcount8M1") write.csv(TPMcount0M2, "CountResult/counts_TPMcount0M2") write.csv(TPMcount1M2, "CountResult/counts_TPMcount1M2") write.csv(TPMcount2M2, "CountResult/counts_TPMcount2M2") write.csv(TPMcount3M2, "CountResult/counts_TPMcount3M2") write.csv(TPMcount4M2, "CountResult/counts_TPMcount4M2") write.csv(TPMcount5M2, "CountResult/counts_TPMcount5M2") write.csv(TPMcount6M2, "CountResult/counts_TPMcount6M2") write.csv(TPMcount7M2, "CountResult/counts_TPMcount7M2") write.csv(TPMcount8M2, "CountResult/counts_TPMcount8M2") #avg.KO.cells <- log1p(AverageExpression(KO.cells, verbose = FALSE)$RNA) #original code log transformed avg.KO.cells <- (AverageExpression(KO.cells, verbose = FALSE)$RNA) avg.KO.cells$gene <- rownames(avg.KO.cells) genes.to.label= ("ncRNA-inter-chr7-5998") #genes.to.label = c("ISG15", "LY6E", "IFI6", "ISG20", "MX1", "IFIT2", "IFIT1", "CXCL10", "CCL8") p1 <- ggplot(avg.KO.cells, aes(CTRL, STIM)) + geom_point() + ggtitle("CD4 Naive T Cells") p1 <- LabelPoints(plot = p1, points = genes.to.label, repel = TRUE) p2 <- ggplot(avg.cd14.mono, aes(CTRL, STIM)) + geom_point() + ggtitle("CD14 Monocytes") p2 <- LabelPoints(plot = p2, points = genes.to.label, repel = TRUE) plot_grid(p1, p2) ``` ```{r} KO.cells <- RunUMAP(KO.cells, reduction = "pca", dims = 1:20 ) KO.cells <- FindNeighbors(KO.cells, reduction = "pca", dims = 1:20) KO.cells <- FindClusters(KO.cells, resolution = 0.5 ) KO.cells <- RunTSNE(KO.cells, reduction = "pca", dims = 1:20) Hep.cells <- RunUMAP(Hep.cells, reduction = "pca", dims = 1:20 ) Hep.cells <- FindNeighbors(Hep.cells, reduction = "pca", dims = 1:20) Hep.cells <- FindClusters(Hep.cells, resolution = 0.5 ) Hep.cells <- RunTSNE(Hep.cells, reduction = "pca", dims = 1:20) # Visualization p1 <- UMAPPlot(KO.cells, reduction = "umap", group.by = "stim") p2 <- UMAPPlot(KO.cells, reduction = "umap", group.by = "mouse.sex") p3 <- UMAPPlot(KO.cells, reduction = "umap", label = TRUE) p4 <- UMAPPlot(KO.cells, label=TRUE) plot_grid(p1,p4) DimPlot(KO.cells, reduction = "umap", split.by = "stim") #hepatocyte cells p5 <- UMAPPlot(Hep.cells, reduction = "umap", group.by = "stim") p6 <- UMAPPlot(Hep.cells, reduction = "umap", group.by = "mouse.sex") p7 <- UMAPPlot(Hep.cells, reduction = "umap", label = TRUE) p8 <- UMAPPlot(Hep.cells, label=TRUE) plot_grid(p5,p8) DimPlot(KO.cells, reduction = "umap", split.by = "stim") raw.data.KO.0 <- as.matrix(GetAssayData(KO.cells, slot = "counts")[, WhichCells(KO.cells, ident = 0)]) raw.data.KO.1 <- as.matrix(GetAssayData(KO.cells, slot = "counts")[, WhichCells(KO.cells, ident = 1)]) raw.data.KO.2 <-as.matrix(GetAssayData(KO.cells, slot = "counts")[, WhichCells(KO.cells, ident = 2)]) raw.data.KO.3 <-as.matrix(GetAssayData(KO.cells, slot = "counts")[, WhichCells(KO.cells, ident = 3)]) raw.data.KO.4 <-as.matrix(GetAssayData(KO.cells, slot = "counts")[, WhichCells(KO.cells, ident = 4)]) raw.data.KO.5 <-as.matrix(GetAssayData(KO.cells, slot = "counts")[, WhichCells(KO.cells, ident = 5)]) write.csv(raw.data.KO.0, "CountResult/Markers/raw.data.KO.0") write.csv(raw.data.KO.1, "CountResult/Markers/raw.data.KO.1") write.csv(raw.data.KO.2, "CountResult/Markers/raw.data.KO.2") write.csv(raw.data.KO.3, "CountResult/Markers/raw.data.KO.3") write.csv(raw.data.KO.4, "CountResult/Markers/raw.data.KO.4") write.csv(raw.data.KO.5, "CountResult/Markers/raw.data.KO.5") f1 <- FeaturePlot(KO.cells, features = c('ncRNA-inter-chr7-5998'), reduction = "umap", split.by = "stim") plot_grid(f1,p1,p4) DefaultAssay(KO.cells) <- "RNA" KO.cells <- NormalizeData(KO.cells, verbose = FALSE) plots <- VlnPlot(KO.cells, features = c("Alb", "ncRNA-inter-chr7-5998","Cyp2b10","Cyp2e1","Cyp2f2"), split.by = "stim", group.by = "seurat_clusters", pt.size = 0, combine = FALSE) CombinePlots(plots = plots, ncol = 1) Three_five_six <- subset(KO.cells, idents = c("5","6")) Three_five_six <- RunUMAP(Three_five_six, reduction = "pca", dims = 1:20 ) Three_five_six <- FindNeighbors(Three_five_six, reduction = "pca", dims = 1:20) Three_five_six <- FindClusters(Three_five_six, resolution = 1 ) Three_five_six <- RunTSNE(Three_five_six, reduction = "pca", dims = 1:20) p1 <- UMAPPlot(Three_five_six, reduction = "umap", group.by = "stim") p2 <- UMAPPlot(Three_five_six, reduction = "umap", group.by = "mouse.sex") p3 <- UMAPPlot(Three_five_six, reduction = "umap", label = TRUE) p4 <- UMAPPlot(Three_five_six, label=TRUE) plot_grid(p1,p4) DimPlot(Three_five_six, reduction = "umap", split.by = "stim") f1 <- FeaturePlot(Three_five_six, features = c('ncRNA-inter-chr7-5998'), reduction = "umap", split.by = "stim") lnc5998 <- subset(combined, cells = lnc5998.cells, idents = "1") lnc5998 <- RunUMAP(lnc5998, reduction = "pca", dims = 1:20 ) lnc5998 <- FindNeighbors(lnc5998, reduction = "pca", dims = 1:20) lnc5998 <- FindClusters(lnc5998, resolution = 1 ) lnc5998 <- RunTSNE(lnc5998, reduction = "pca", dims = 1:20) # Visualization p1 <- UMAPPlot(lnc5998, reduction = "umap", group.by = "stim") p2 <- UMAPPlot(lnc5998, reduction = "umap", group.by = "mouse.sex") p3 <- UMAPPlot(lnc5998, reduction = "umap", label = TRUE) p4 <- UMAPPlot(lnc5998, label=TRUE) plot_grid(p1,p4) DimPlot(lnc5998, reduction = "umap", split.by = "stim") lnc5998.cells <- WhichCells(object = combined, expression = "ncRNA-inter-chr7-5998" > 1) FeaturePlot(lnc5998, features = c("ncRNA-inter-chr7-5998"), split.by = "stim", + cols = c("grey", "red"), cells = lnc5998.cells,min.cutoff = 0.5) DefaultAssay(lnc5998) <- "RNA" lnc5998 <- NormalizeData(lnc5998, verbose = FALSE) plots <- VlnPlot(lnc5998, features = c("Alb", "ncRNA-inter-chr7-5998","Cyp2b10","Cyp2e1","Cyp2f2"), split.by = "stim", group.by = "seurat_clusters", pt.size = 0, combine = FALSE) CombinePlots(plots = plots, ncol = 1) ``` ```{r} d <- dist(t(GetAssayData(KO.cells, slot = "scale.data"))) # Run the MDS procedure, k determines the number of dimensions mds <- cmdscale(d = d, k = 2) # cmdscale returns the cell embeddings, we first label the columns to ensure downstream # consistency colnames(mds) <- paste0("MDS_", 1:2) # We will now store this as a custom dimensional reduction called 'mds' KO.cells[["mds"]] <- CreateDimReducObject(embeddings = mds, key = "MDS_", assay = DefaultAssay(KO.cells)) # We can now use this as you would any other dimensional reduction in all downstream functions DimPlot(KO.cells, reduction = "mds", pt.size = 0.5) ``` Find differential markers ```{r} KO.cells$celltype.stim <- paste(Idents(KO.cells), KO.cells$stim, sep = "_") KO.cells$celltype <- Idents(KO.cells) Idents(KO.cells) <- "celltype.stim" response3 <- FindMarkers(KO.cells, ident.1 = c("1_G171B","0_G171B","2_G171B"), ident.2 = c("1_G171C", "0_G171C","2_G171C"), verbose = TRUE, test.use = "MAST", logfc.threshold = FALSE,min.pct = FALSE) head(response3, n = 15) #DE1: Compare cluster 0+1+12 (that expressed lnc5998) with Other hepatocyte clusters (2+5+8) #DE2: compare cluster 1 (showed major effects in the KD) vs Cluster 0 (that showed little KD) #DE3: For KO.cells that formed five subcluster, compare clusters 3+4+5+1 vs 2+0 #DE4: for KO.cells that formed five clusters. Comapre cluster 4 vs. 2 DE0112.258 <- FindMarkers(combined, ident.1 = c("0","1","12" ), ident.2 = c("2","5","8"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DE0112.All <- FindMarkers(combined, ident.1 = c("0","1","12" ), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DE1.2 <- FindMarkers(combined, ident.1 = c("1" ), ident.2 = c("2"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DE1.5 <- FindMarkers(combined, ident.1 = c("1" ), ident.2 = c("5"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DE1.8 <- FindMarkers(combined, ident.1 = c("1" ), ident.2 = c("8"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DE2.5 <- FindMarkers(combined, ident.1 = c("2" ), ident.2 = c("5"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DE2.8 <- FindMarkers(combined, ident.1 = c("2" ), ident.2 = c("8"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DE5.8 <- FindMarkers(combined, ident.1 = c("5" ), ident.2 = c("8"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DEK4351.20 <- FindMarkers(KO.cells, ident.1 = c("3","4","5","1" ), ident.2 = c("2","0"),verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DEK4.3 <- FindMarkers(KO.cells, ident.1 = "4", ident.2 = "3",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DEK4.2 <- FindMarkers(KO.cells, ident.1 = "4", ident.2 = "2",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DEK4.0 <- FindMarkers(KO.cells, ident.1 = "4", ident.2 = "0",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DEK4.1 <- FindMarkers(KO.cells, ident.1 = "4", ident.2 = "1",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DEK4.5 <- FindMarkers(KO.cells, ident.1 = "4", ident.2 = "5",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) ##### comaprison between G171B vs G171C for KO.cell clusters of 0+1+12 ######33 DEK4_C.B <- FindMarkers(KO.cells, ident.1 = "4_G171C", ident.2 = "4_G171B",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DEK3_C.B <- FindMarkers(KO.cells, ident.1 = "3_G171C", ident.2 = "3_G171B",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DEK1_C.B <- FindMarkers(KO.cells, ident.1 = "1_G171C", ident.2 = "1_G171B",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DEK2_C.B <- FindMarkers(KO.cells, ident.1 = "2_G171C", ident.2 = "2_G171B",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DEK0_C.B <- FindMarkers(KO.cells, ident.1 = "0_G171C", ident.2 = "0_G171B",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) DEK5_C.B <- FindMarkers(KO.cells, ident.1 = "5_G171C", ident.2 = "5_G171B",verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) ################################### Write the results ######################### write.csv(DE0112.258, "CountResult/Markers/DE0112.258") write.csv(DE0112.All, "CountResult/Markers/DE0112.All") write.csv(DE1.2, "CountResult/Markers/DE1.2") write.csv(DE1.5, "CountResult/Markers/DE1.5") write.csv(DE1.8, "CountResult/Markers/DE1.8") write.csv(DE2.5, "CountResult/Markers/DE2.5") write.csv(DE2.8, "CountResult/Markers/DE2.8") write.csv(DE5.8, "CountResult/Markers/DE5.8") write.csv(DEK4351.20, "CountResult/Markers/DEK4351.20") write.csv(DEK4.3, "CountResult/Markers/DEK4.3") write.csv(DEK4.2, "CountResult/Markers/DEK4.2") write.csv(DEK4.0, "CountResult/Markers/DEK4.0") write.csv(DEK4.1, "CountResult/Markers/DEK4.1") write.csv(DEK4.5, "CountResult/Markers/DEK4.5") write.csv(DEK4_C.B, "CountResult/Markers/DEK4_C.B") write.csv(DEK3_C.B, "CountResult/Markers/DEK3_C.B") write.csv(DEK1_C.B, "CountResult/Markers/DEK1_C.B") write.csv(DEK2_C.B, "CountResult/Markers/DEK2_C.B") write.csv(DEK0_C.B, "CountResult/Markers/DEK0_C.B") write.csv(DEK5_C.B, "CountResult/Markers/DEK5_C.B") combined$celltype.stim <- paste(Idents(combined), combined$stim, sep = "_") combined$celltype <- Idents(combined) Idents(combined) <- "celltype.stim" test.combined$celltype.stim <- paste(Idents(test.combined), test.combined$stim, sep = "_") test.combined$celltype <- Idents(test.combined) Idents(test.combined) <- "celltype.stim" Combined_G171B_vs_G171C <- FindMarkers(combined, ident.1 = c("0_G171B","1_G171B","2_G171B","3_G171B","4_G171B","5_G171B","6_G171B","7_G171B","8_G171B","9_G171B","10_G171B","11_G171B","12_G171B" ), ident.2 = c("0_G171C","1_G171C","2_G171C","3_G171C","4_G171C","5_G171C","6_G171C","7_G171C","8_G171C","9_G171C","10_G171C","11_G171C","12_G171C" ), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) Combined_G171C_vs_G171B_Clust1 <- FindMarkers(combined, ident.1 = c("1_G171C"), ident.2 = c("1_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) Combined_G171C_vs_G171B_Clust0 <- FindMarkers(combined, ident.1 = c("0_G171C"), ident.2 = c("0_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) Combined_G171C_vs_G171B_Clust12 <- FindMarkers(combined, ident.1 = c("12_G171C"), ident.2 = c("12_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) Combined_G171C_vs_G171B_Clust2 <- FindMarkers(combined, ident.1 = c("2_G171C"), ident.2 = c("2_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) Combined_G171C_vs_G171B_Clust5 <- FindMarkers(combined, ident.1 = c("5_G171C"), ident.2 = c("5_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) Combined_G171C_vs_G171B_Clust8 <- FindMarkers(combined, ident.1 = c("8_G171C"), ident.2 = c("8_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) Combined_G171C_vs_G171B_Clust3 <- FindMarkers(combined, ident.1 = c("3_G171C"), ident.2 = c("3_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) Combined_G171C_vs_G171B_Clust4 <- FindMarkers(combined, ident.1 = c("4_G171C"), ident.2 = c("4_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) Combined_G171C_vs_G171B_Clust6 <- FindMarkers(combined, ident.1 = c("6_G171C"), ident.2 = c("6_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) Combined_G171C_vs_G171B_Clust7 <- FindMarkers(combined, ident.1 = c("7_G171C"), ident.2 = c("7_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) Combined_G171C_vs_G171B_Clust9 <- FindMarkers(combined, ident.1 = c("9_G171C"), ident.2 = c("9_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) Combined_G171C_vs_G171B_Clust10 <- FindMarkers(combined, ident.1 = c("10_G171C"), ident.2 = c("10_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) Combined_G171C_vs_G171B_Clust11 <- FindMarkers(combined, ident.1 = c("11_G171C"), ident.2 = c("11_G171B"), verbose = TRUE, logfc.threshold = FALSE,min.pct = FALSE) write.csv(Combined_G171C_vs_G171B_Clust1, "CountResult/Markers/Combined_G171C_vs_G171B_Clust1") write.csv(Combined_G171C_vs_G171B_Clust0, "CountResult/Markers/Combined_G171C_vs_G171B_Clust0") write.csv(Combined_G171C_vs_G171B_Clust12, "CountResult/Markers/Combined_G171C_vs_G171B_Clust12") write.csv(Combined_G171C_vs_G171B_Clust2, "CountResult/Markers/Combined_G171C_vs_G171B_Clust2") write.csv(Combined_G171C_vs_G171B_Clust5, "CountResult/Markers/Combined_G171C_vs_G171B_Clust5") write.csv(Combined_G171C_vs_G171B_Clust8, "CountResult/Markers/Combined_G171C_vs_G171B_Clust8") write.csv(Combined_G171C_vs_G171B_Clust3, "CountResult/Markers/Combined_G171C_vs_G171B_Clust3") write.csv(Combined_G171C_vs_G171B_Clust4, "CountResult/Markers/Combined_G171C_vs_G171B_Clust4") write.csv(Combined_G171C_vs_G171B_Clust6, "CountResult/Markers/Combined_G171C_vs_G171B_Clust6") write.csv(Combined_G171C_vs_G171B_Clust7, "CountResult/Markers/Combined_G171C_vs_G171B_Clust7") write.csv(Combined_G171C_vs_G171B_Clust9, "CountResult/Markers/Combined_G171C_vs_G171B_Clust9") write.csv(Combined_G171C_vs_G171B_Clust10, "CountResult/Markers/Combined_G171C_vs_G171B_Clust10") write.csv(Combined_G171C_vs_G171B_Clust11, "CountResult/Markers/Combined_G171C_vs_G171B_Clust11") PC_vs_PP_G171B <- FindMarkers(KO.cells, ident.1 = c("4_G171B","3_G171B"), ident.2 = c("0_G171B","2_G171B"), verbose = TRUE, test.use = "MAST", logfc.threshold = FALSE,min.pct = FALSE) head(PC_vs_PP_G171B, n = 15) PC_vs_PP_G171C <- FindMarkers(KO.cells, ident.1 = c("4_G171C","3_G171C"), ident.2 = c("0_G171C","2_G171C"), verbose = TRUE, test.use = "MAST", logfc.threshold = FALSE,min.pct = FALSE) head(PC_vs_PP_G171C, n = 15) PC_vs_PP_G171BC <- FindMarkers(KO.cells, ident.1 = c("4_G171B","3_G171B","4_G171C","3_G171C"), ident.2 = c("0_G171B","2_G171B","0_G171C","2_G171C"), verbose = TRUE, test.use = "MAST", logfc.threshold = FALSE,min.pct = FALSE) head(PC_vs_PP_G171C, n = 15) lnc5998_KO_DE_1 <- FindMarkers(KO.cells, ident.1 = c("4_G171B","3_G171B","5_G171B"), ident.2 = c("4_G171C","3_G171C","5_G171C"), verbose = TRUE, test.use = "MAST", logfc.threshold = FALSE,min.pct = FALSE) head(lnc5998_KO_DE, n = 15) lnc5998_KO_DE_2 <- FindMarkers(KO.cells, ident.1 = c("4_G171C","3_G171C","5_G171C"), ident.2 =c("4_G171B","3_G171B","5_G171B") , verbose = TRUE, test.use = "MAST", logfc.threshold = FALSE,min.pct = FALSE) head(lnc5998_KO_DE, n = 15) cell.type.genes <- (PC_vs_PP_G171BC[1]) # Takes all the unique cell type specific genes GOterms = topGOterms(fg.genes = cell.type.genes, bg.genes = rownames(KO.cells@assays$RNA@dataKO.cells@assays$RNA@data), organism = "Mouse") cell.type.genes <- (PC_vs_PP_G171BC[1]) # Takes all the unique cell type specific genes GOterms = topGOterms(fg.genes = rownames(cell.type.genes), bg.genes = rownames(KO.cells@assays$RNA@data), organism = "Mouse") ``` ```{r} AvergeExpression2 <- function (object, assays = NULL, features = NULL, return.seurat = FALSE, add.ident = NULL, slot = "data", use.scale = FALSE, use.counts = FALSE, verbose = TRUE, ...) { fxn.average <- switch(EXPR = slot, data = function(x) { return(mean(x = x)) }, mean) object.assays <- FilterObjects(object = object, classes.keep = "Assay") assays <- assays %||% object.assays ident.orig <- Idents(object = object) orig.levels <- levels(x = Idents(object = object)) ident.new <- c() if (!all(assays %in% object.assays)) { assays <- assays[assays %in% object.assays] if (length(assays) == 0) { stop("None of the requested assays are present in the object") } else { warning("Requested assays that do not exist in object. Proceeding with existing assays only.") } } if (!is.null(x = add.ident)) { new.data <- FetchData(object = object, vars = add.ident) new.ident <- paste(Idents(object)[rownames(x = new.data)], new.data[, 1], sep = "_") Idents(object, cells = rownames(new.data)) <- new.ident } data.return <- list() for (i in 1:length(x = assays)) { data.use <- GetAssayData(object = object, assay = assays[i], slot = slot) features.assay <- features if (length(x = intersect(x = features, y = rownames(x = data.use))) < 1) { features.assay <- rownames(x = data.use) } data.all <- data.frame(row.names = features.assay) for (j in levels(x = Idents(object))) { temp.cells <- WhichCells(object = object, idents = j) features.assay <- unique(x = intersect(x = features.assay, y = rownames(x = data.use))) if (length(x = temp.cells) == 1) { data.temp <- (data.use[features.assay, temp.cells]) if (slot == "data") { data.temp <- data.temp } } if (length(x = temp.cells) > 1) { data.temp <- apply(X = data.use[features.assay, temp.cells, drop = FALSE], MARGIN = 1, FUN = fxn.average) } data.all <- cbind(data.all, data.temp) colnames(x = data.all)[ncol(x = data.all)] <- j if (verbose) { message(paste("Finished averaging", assays[i], "for cluster", j)) } if (i == 1) { ident.new <- c(ident.new, as.character(x = ident.orig[temp.cells[1]])) } } names(x = ident.new) <- levels(x = Idents(object)) data.return[[i]] <- data.all names(x = data.return)[i] <- assays[[i]] } if (return.seurat) { toRet <- CreateSeuratObject(counts = data.return[[1]], project = "Average", assay = names(x = data.return)[1], ...) if (length(x = data.return) > 1) { for (i in 2:length(x = data.return)) { toRet[[names(x = data.return)[i]]] <- CreateAssayObject(counts = data.return[[i]]) } } if (DefaultAssay(object = object) %in% names(x = data.return)) { DefaultAssay(object = toRet) <- DefaultAssay(object = object) } Idents(toRet, cells = colnames(x = toRet)) <- ident.new[colnames(x = toRet)] Idents(object = toRet) <- factor(x = Idents(object = toRet), levels = as.character(x = orig.levels), ordered = TRUE) toRet <- NormalizeData(object = toRet, verbose = verbose) toRet <- ScaleData(object = toRet, verbose = verbose) return(toRet) } else { return(data.return) } } ``` Find differential expression markers ```{r} combined.markers <- FindAllMarkers(object = combined, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25) combined.markers %>% group_by(cluster) %>% top_n(2, avg_logFC) KO.markers <- FindAllMarkers(object = KO.cells, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25) response3 <- FindMarkers(combined, ident.1 = c("1_G171B","0_G171B","2_G171B"), ident.2 = c("1_G171C", "0_G171C","2_G171C"), verbose = TRUE, test.use = "MAST", logfc.threshold = FALSE,min.pct = FALSE) head(response3, n = 15) ``` Visualize top genes in principal components ```{r, echo=FALSE, fig.height=4, fig.width=8} PCHeatmap(object = tiss1, pc.use = 1:3, cells.use = 500, do.balanced = TRUE, label.columns = FALSE, num.genes = 8) ``` Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus. ```{r} PCElbowPlot(object = tiss1) ``` Choose the number of principal components to use. ```{r} # Set number of principal components. n.pcs = 10 ``` The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity--more connections within the group than between groups. The resolution parameter determines the scale. Higher resolution will give more clusters, lower resolution will give fewer. For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below. ```{r} # Set resolution res.used <- 4 tiss1 <- FindClusters(object = tiss1, reduction.type = "pca", dims.use = 1:n.pcs, resolution = res.used, print.output = 0, save.SNN = TRUE, force.recalc = TRUE) ``` We use TSNE solely to visualize the data. ```{r} # If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10). tiss1 <- RunTSNE(object = tiss1, dims.use = 1:n.pcs, seed.use = 10, perplexity=30) ``` ```{r} TSNEPlot(object = tiss1, do.label = T, pt.size = 1.2, label.size = 4) ``` ## Compare to previous annotations ```{r} previous_annotation = read.csv("/Users/kkarri/Documents/Lab/Single_cell_project/dropseq/Liver_droplet_annotation.csv", stringsAsFactors = FALSE) cols = c('free_annotation', 'cell_ontology_class') for (col in cols){ previous_col = paste0('previous_', col) tiss1@meta.data[, previous_col] <- "NA" tiss1@meta.data[as.character(previous_annotation$X), previous_col] <- previous_annotation[, col] print(table(tiss1@meta.data[, previous_col])) print(table(tiss1@meta.data[, previous_col], tiss@ident)) } tiss1 = compare_previous_annotation(tiss1, tissue_of_interest, "droplet") TSNEPlot(object = tiss1, do.return = TRUE, group.by = "previous_cell_ontology_class") table(tiss1@meta.data[, "previous_cell_ontology_class"], tiss@ident) ``` ```{r} tiss1 = compare_previous_annotation(tiss1, tissue_of_interest, "droplet") TSNEPlot(object = tiss1, do.return = TRUE, group.by = "previous_cell_ontology_class") table(tiss1@meta.data[, "previous_cell_ontology_class"], tiss1@ident) ``` ```{r} TSNEPlot(tiss1, group.by="mouse.sex") TSNEPlot(tiss1, group.by="mouse.id") ``` Significant genes: hepatocyte: Alb, Ttr, Apoa1, and Serpina1c pericentral: Cyp2e1, Glul, Oat, Gulo midlobular: Ass1, Hamp, Gstp1, Ubb periportal: Cyp2f2, Pck1, Hal, Cdh1 endothelial cells: Pecam1, Nrp1, Kdr+ and Oit3+ Kuppfer cells: Emr1, Clec4f, Cd68, Irf7 NK/NKT cells: Zap70, Il2rb, Nkg7, Cxcr6, Klr1c, Gzma B cells: Cd79a, Cd79b, Cd74 and Cd19 Immune cells: Ptprc ```{r, echo=FALSE, fig.height=16, fig.width=12} # Hepatic marker FeaturePlot(tiss1, c(genes_hep), pt.size = 1, nCol = 4, cols.use = c("grey", "red")) # Endothelial markers FeaturePlot(tiss1, c(genes_endo), pt.size = 1, nCol = 4, cols.use = c("grey", "red")) # Kupffer cells FeaturePlot(tiss1, c(genes_kuppfer), pt.size = 1, nCol = 4, cols.use = c("grey", "red")) # genes_nk FeaturePlot(tiss1, c(genes_nk), pt.size = 1, nCol = 4, cols.use = c("grey", "red")) # genes_b FeaturePlot(tiss1, c(genes_b), pt.size = 1, nCol = 4, cols.use = c("grey", "red")) # genes bile duct endo cells FeaturePlot(tiss1, c(genes_bec), pt.size = 1, nCol = 4, cols.use = c("grey", "red")) # genes immune FeaturePlot(tiss1, c(genes_immune), pt.size = 1, nCol = 4, cols.use = c("grey", "red")) ``` Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest. The radius shows you the percent of cells in that cluster with at least one read sequenced from that gene. The color level indicates the average Z-score of gene expression for cells in that cluster, where the scaling is done over taken over all cells in the sample. #We have various immune cell types in the last cluster ```{r, echo=FALSE, fig.height=4, fig.width=10} DotPlot(tiss1, c(genes_kuppfer, genes_nk, genes_b, "Ptprc"), plot.legend = T, col.max = 2.5, do.return = T) + coord_flip() ``` ```{r, echo=FALSE, fig.height=8, fig.width=10} DotPlot(tiss1, c(genes_hep_main, genes_endo, genes_nk, genes_kuppfer, genes_bec_b_immune), plot.legend = T, col.max = 2.5, do.return = T) + coord_flip() ``` Using the markers above, we can confidentaly label many of the clusters: 19: endothelial cells 20: bile duct epithelial cells 21: immune cells rest are hepatocytes We will add those cell_ontology_classes to the dataset. ```{r} tiss1 <- StashIdent(object = tiss1, save.name = "cluster.ids") cluster.ids <- c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20) free_annotation <- c( NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "bile duct epithelial cells", "endothelial cell of hepatic sinusoid", NA ) cell_ontology_class <- c( "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "duct epithelial cell", "endothelial cell of hepatic sinusoid", "hepatocyte") tiss1 = stash_annotations(tiss1, cluster.ids, free_annotation, cell_ontology_class) ``` ## Checking for batch effects Color by metadata, like plate barcode, to check for batch effects. ```{r} TSNEPlot(object = tiss1, do.return = TRUE, group.by = "channel") TSNEPlot(object = tiss1, do.return = TRUE, group.by = "free_annotation") ``` ## Subcluster Let's drill down on the hepatocytes. ```{r} subtiss1 = SubsetData(tiss1, ident.use = c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,20)) ``` ```{r} subtiss1 <- subtiss1 %>% ScaleData() %>% FindVariableGenes(do.plot = FALSE, x.high.cutoff = Inf, y.cutoff = 0.5) %>% RunPCA(do.print = FALSE) ``` ```{r} PCHeatmap(object = subtiss1, pc.use = 1:3, cells.use = 20, do.balanced = TRUE, label.columns = FALSE, num.genes = 8) PCElbowPlot(subtiss1) ``` ```{r} sub.n.pcs = 8 sub.res.use = 0.5 subtiss1 <- subtiss1 %>% FindClusters(reduction.type = "pca", dims.use = 1:sub.n.pcs, resolution = sub.res.use, print.output = 0, save.SNN = TRUE, force.recalc = TRUE) %>% RunTSNE(dims.use = 1:sub.n.pcs, seed.use = 10, perplexity=8) TSNEPlot(object = subtiss1, do.label = T, pt.size = 1, label.size = 4) ``` ```{r, echo=FALSE, fig.height=25, fig.width=25} FeaturePlot(subtiss1, genes_hep,cols.use = c("grey", "red"), pt.size = 4, nCol = 4) ``` ```{r, echo=FALSE, fig.height=8, fig.width=10} DotPlot(subtiss1, all_genes, col.max = 2.5, plot.legend = T, do.return = T) + coord_flip() ``` ```{r} BuildClusterTree(subtiss1) ``` ```{r, echo=FALSE, fig.height=10, fig.width=8} #female genes have lower expression in cluster 6 relative to other female clusters, especally Xist FeaturePlot(subtiss1,c('Mup20', 'Mup1','Mup12', 'Mup21', 'Cyp2d9', 'Xist', 'A1bg', 'Cyp2c69'),cols.use = c("grey", "red"), pt.size = 3, nCol = 2) DotPlot(tiss1,c('Mup20', 'Mup1','Mup12', 'Mup21', 'Cyp2d9', 'Xist', 'A1bg', 'Cyp2c69'), plot.legend = T, col.max = 2.5, do.return = T) + coord_flip() ``` From these genes, it appears that the clusters represent: 0: midlobular male 1: pericentral female 2: periportal female 3: periportal male 4: midlobular male 5: pericentral male 6: midlobular female 7: midlobular female The multitude of clusters of each type correspond mostly to individual animals/sexes. ```{r} table(FetchData(subtiss1, c('mouse.sex','ident')) %>% droplevels()) ``` ```{r} sub.cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7) sub.free_annotation <- c("periportal female", "midlobular male", "pericentral female", "periportal male", "midlobular male", "pericentral male", "midlobular female", "midlobular female") sub.cell_ontology_class <- c("hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte") subtiss1 = stash_annotations(subtiss1, sub.cluster.ids, sub.free_annotation, sub.cell_ontology_class) tiss1 = stash_subtiss_in_tiss(tiss1, subtiss1) ``` Liver zonation markers ```{r} genes_zones = c('Cyp2e1', 'Glul', 'Oat', 'Gulo', 'Ass1', 'Hamp', 'Gstp1', 'Ubb', 'Cyp2f2', 'Pck1', 'Hal', 'Cdh1') FeaturePlot(subtiss1,c(genes_zones),cols.use = c("grey", "red"), pt.size = 1, nCol = 4) DotPlot(subtiss1,c(genes_zones), plot.legend = T, col.max = 2.5, do.return = T) + coord_flip() TSNEPlot(object = subtiss1, do.label = T, pt.size = 1, label.size = 4, group.by="free_annotation") TSNEPlot(object = tiss1, do.label = T, pt.size = 1, label.size = 4, group.by="free_annotation") ``` ########## Find cluster markers for lncRNAs ```{r} MIN_LOGFOLD_CHANGE = 1 # set to minimum required average log fold change in gene expression. MIN_PCT_CELLS_EXPR_GENE = 0.1 all.markers = FindAllMarkers(tiss1, min.pct = MIN_PCT_CELLS_EXPR_GENE, logfc.threshold = MIN_LOGFOLD_CHANGE, only.pos = TRUE, test.use="bimod") # likelihood ratio test lnc_all_markers <- grep(pattern = "^ncRNA", x= rownames(all.markers), value = TRUE) lnc_all_markers #[1] "ncRNA_inter_chr10_92081" "ncRNA_intra_chr16_13383" "ncRNA_inter_chr17_13605" "ncRNA_inter_chr14_11815" #[5] "ncRNA_inter_chr18_14344" FeaturePlot(subtiss1,c(lnc_all_markers),cols.use = c("grey", "red"), pt.size = 1, nCol = 4) ######################### lncRNA markers- CELL TYPE MARKER ############ markers.hep <- FindMarkers(object = tiss1, ident.1 = c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,20), ident.2 = c(18,19),only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25) lnc_markers_hep <- grep(pattern = "^ncRNA", x= rownames(markers.hep), value = TRUE) lnc_markers_hep FeaturePlot(tiss1,c(lnc_markers_hep),cols.use = c("grey", "red"), pt.size = 1, nCol = 4) DotPlot(tiss1,lnc_markers_hep, plot.legend = T, col.max = 2.5, do.return = T) + coord_flip() #[1] "ncRNA_as_chr11_9423" "ncRNA_as_chr7_6166" "ncRNA_inter_chr4_3295" "ncRNA_inter_chr17_14026" #[5] "ncRNA_inter_chr3_2915" "ncRNA_inter_chr5_4547" "ncRNA_inter_chr15_12684" markers.hep.MAST <- FindMarkers(object = tiss1, ident.1 = c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,20), ident.2 = c(18,19),only.pos = TRUE, test.use = "MAST") lnc_markers_hep_MAST_TABLE <- subset(markers.hep.MAST, grepl("^ncRNA", rownames(markers.hep.MAST))) lnc_markers_hep_MAST <- grep(pattern = "^ncRNA", x= rownames(markers.hep.MAST), value = TRUE) lnc_markers_hep_MAST markers.endo <- FindMarkers(object = tiss1, ident.1 = c(18,19), only.pos = TRUE, min.pct = 0.25, thresh.use = 0.5) lnc_markers_endo <- grep(pattern = "^ncRNA", x= rownames(markers.endo), value = TRUE) lnc_markers_endo FeaturePlot(tiss1,c(lnc_markers_endo),cols.use = c("grey", "red"), pt.size = 1, nCol = 4) DotPlot(tiss1,lnc_markers_endo, plot.legend = T, col.max = 2.5, do.return = T) + coord_flip() #"ncRNA_inter_chr15_12770", "ncRNA_inter_chr12_10817", "ncRNA_as_chr13_11451", markers.endo.MAST <- FindMarkers(object = tiss1, ident.1 = 19, test.use = "MAST" ,only.pos = TRUE) lnc_markers_endo_MAST_TABLE <- subset(markers.endo.MAST, grepl("^ncRNA", rownames(markers.endo.MAST))) lnc_markers_endo_MAST <- grep(pattern = "^ncRNA", x= rownames(markers.endo.MAST), value = TRUE) lnc_markers_endo_MAST ################## lncRNA expression ########################3 # "ncRNA_inter_chr17_13605" , "ncRNA_intra_chr16_13383" ########## Periporal markers- zonation markers ############ markers.pc <- FindMarkers(object = subtiss1, ident.1 = c(2,5), only.pos = FALSE, min.pct = 0.001, thresh.use = 0.001, test.use = "bimod" ) markers.pc.MAST <- FindMarkers(object = subtiss1, ident.1 = c(2,5), ident.2 = c(0,3), test.use = "MAST" ,only.pos = TRUE) lnc_markers_pc <- subset(markers.pc, grepl("^ncRNA", rownames(markers.pc))) lnc_markers_pc <- grep(pattern = "^ncRNA", x= rownames(markers.pc), value = TRUE) lnc_markers_pc markers.pc.MAST <- FindMarkers(object = subtiss1, ident.1 = c(2,5), ident.2 = c(0,3), test.use = "MAST" ,only.pos = TRUE) lnc_markers_pc_MAST <- subset(markers.pc.MAST, grepl("^ncRNA", rownames(markers.pc.MAST))) lnc_markers_pc_MAST <- grep(pattern = "^ncRNA", x= rownames(markers.pc.MAST), value = TRUE) lnc_markers_pc_MAST DotPlot(tiss1, lnc_markers_pc, plot.legend = T, col.max = 2.5, do.return = T, group.by="free_annotation") + coord_flip() FeaturePlot(subtiss1,c(lnc_markers_pc),cols.use = c("grey", "red"), pt.size = 1, nCol = 4) ############################### midlobular genes ############# markers.mid <- FindMarkers(object = subtiss1, ident.1 = c(1,4,6,7), only.pos = FALSE, min.pct = 0.001, thresh.use = 0.05) lnc_markers_mid <- subset(markers.mid, grepl("^ncRNA", rownames(markers.mid))) lnc_markers_mid <- grep(pattern = "^ncRNA", x= rownames(markers.mid), value = TRUE) lnc_markers_mid DotPlot(tiss1, lnc_markers_mid, plot.legend = T, col.max = 2.5, do.return = T, group.by="free_annotation") + coord_flip() FeaturePlot(subtiss1,c(lnc_markers_mid),cols.use = c("grey", "red"), pt.size = 1, nCol = 4) markers.mid.MAST <- FindMarkers(object = subtiss1, ident.1 = c(1,4,6,7),test.use = "MAST",only.pos = TRUE ) lnc_markers_mid_MAST_TABLE <- subset(markers.mid.MAST, grepl("^ncRNA", rownames(markers.mid.MAST))) lnc_markers_mid_MAST <- grep(pattern = "^ncRNA", x= rownames(markers.mid.MAST), value = TRUE) lnc_markers_mid_MAST #####3 periportalmarker genes############3 markers.pp <- FindMarkers(object = subtiss1, ident.1 = c(0,3), only.pos = FALSE, min.pct = 0.001, thresh.use = 0.05) lnc_markers_pp <- subset(markers.pp, grepl("^ncRNA", rownames(markers.pp))) lnc_markers_pp <- grep(pattern = "^ncRNA", x= rownames(markers.pp), value = TRUE) lnc_markers_pp markers.pp.MAST <- FindMarkers(object = subtiss1, ident.1 = c(0,3), ident.2 = c(2,5),test.use = "MAST",only.pos = TRUE ) lnc_markers_pp_MAST_TABLE <- subset(markers.pp.MAST, grepl("^ncRNA", rownames(markers.pp.MAST))) lnc_markers_pp_MAST <- grep(pattern = "^ncRNA", x= rownames(markers.pp.MAST), value = TRUE) lnc_markers_pp_MAST FeaturePlot(subtiss1,c(lnc_markers_pp),cols.use = c("grey", "red"), pt.size = 1, nCol = 4, max.cutoff = 1) DotPlot(tiss1, c(lnc_markers_pp,"Cyp2e1","Cyp2f2"), plot.legend = T, col.max = 2.5, do.return = T, group.by= "free_annotation") + coord_flip() ################## amle and female specific ############################ markers.female <- FindMarkers(object = subtiss1, ident.1 = c(0,2,6,7), only.pos = TRUE, min.pct = 0.1, logfc.threshold = 1) lnc_markers_female <- subset(markers.female, grepl("^ncRNA", rownames(markers.female))) lnc_markers_female <- grep(pattern = "^ncRNA", x= rownames(markers.female), value = TRUE) lnc_markers_female FeaturePlot(subtiss1,c(lnc_markers_female),cols.use = c("grey", "red"), pt.size = 1, nCol = 4, max.cutoff = 1) DotPlot(tiss1, c(lnc_markers_female,"Cyp2e1","Cyp2f2"), plot.legend = T, col.max = 2.5, do.return = T, group.by= "free_annotation") + coord_flip() markers.male <- FindMarkers(object = subtiss1, ident.1 = c(1,3,4,5), only.pos = TRUE, min.pct = 0.001, thresh.use = 0.05) lnc_markers_male <- subset(markers.male, grepl("^ncRNA", rownames(markers.male))) lnc_markers_male <- grep(pattern = "^ncRNA", x= rownames(markers.male), value = TRUE) lnc_markers_male FeaturePlot(subtiss1,c(lnc_markers_male),cols.use = c("grey", "red"), pt.size = 1, nCol = 4, max.cutoff = 1) DotPlot(tiss1, c(lnc_markers_male,"Cyp2e1","Cyp2f2"), plot.legend = T, col.max = 2.5, do.return = T, group.by= "free_annotation") + coord_flip() ############################ Female zonate specific genes ################################### markers.pericentral.female <- FindMarkers(object = tiss1, ident.1 = c(6,11,14,20), test.use = "MAST", only.pos = TRUE, min.pct = 0.1, ident.2 = c(2,3,15,12,13,8,5,16), logfc.threshold = 1) markers.periportal.female <- FindMarkers(object = tiss1, ident.1 = c(2,3,15), only.pos = TRUE, min.pct = 0.1, ident.2 = c(6,11,14,20,12,13,8,5,16), logfc.threshold = 1) markers.pericentral.male <- FindMarkers(object = tiss1, ident.1 = c(13,12), test.use = "MAST", only.pos = TRUE, min.pct = 0.1, ident.2 = c(2,3,15,8,5,16,6,11,14,20), logfc.threshold = 1) markers.periportal.male <- FindMarkers(object = tiss1, ident.1 = c(8,5,16), test.use = "MAST", only.pos = TRUE, min.pct = 0.1, ident.2 = c(2,3,15,13,12,6,11,14,20), logfc.threshold = 1) ############### xeno-lncs CAR?RXR ################## FeaturePlot(tiss1,c("ncRNA_inter_chr15_12684","ncRNA_inter_chr8_7430","ncRNA_inter_chr7_6222"),cols.use = c("grey", "red"), pt.size = 1, nCol = 4, max.cutoff = 1) ####################################################################### markers.endo.2 <- FindMarkers(object = seurat_drop, logfc.threshold = 2,ident.1 = "Endothelial", only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25) lnc.endo.2 <- grep(pattern = "^ncRNA", x= rownames(markers.endo.2), value = TRUE) lnc.endo.2 ``` Zonated lncRNAs ```{r} pp_zontaed <- c('ncRNA_inter_chr14_12016','ncRNA_as_chr19_15090','ncRNA_inter_chr10_9351','ncRNA_inter_chr16_13170', 'ncRNA_inter_chr3_2697','ncRNA_inter_chr1_274','ncRNA_as_chr6_5518','ncRNA_inter_chr14_12066','ncRNA_intra_chr12_10871', 'ncRNA_inter_chr16_13510','ncRNA_inter_chr3_2314','ncRNA_inter_chr10_9264,'ncRNA_inter_chr9_8122') ``` ## Checking for batch effects Color by metadata, like plate barcode, to check for batch effects. ```{r} TSNEPlot(object = subtiss1, do.return = TRUE, group.by = "mouse.sex") ``` # Final coloring Color by cell ontology class on the original TSNE. ```{r} TSNEPlot(object = tiss1, do.return = TRUE, group.by = "cell_ontology_class") ``` # Save the Robject for later ```{r} filename = here('00_data_ingest', '04_tiss1ue_robj_generated', paste0("droplet_", tiss1ue_of_interest, "refinedcells_seurat_tiss1.Robj")) print(filename) save(tiss1, file=filename) ``` ```{r} # To reload a saved object filename = here('00_data_ingest', '04_tiss1ue_robj_generated', paste0("droplet_", tissue_of_interest, "seurat_smartdrop-integrated-8272019.Robj")) load(file=filename) ``` # Export the final metadata ```{r} save_annotation_csv(tiss1, tiss1ue_of_interest, "droplet") ```