--- 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) ########## 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$tech <- "droplet" #droplet <- SubsetData(droplet,subset.names = c("nGene", "nUMI"), low.thresholds = c(500, 1000)) # old version of seurat droplet <- subset(droplet, subset = nFeature_RNA > 500 & nCount_RNA > 1000) droplet <- NormalizeData(droplet, verbose = FALSE) droplet <- FindVariableFeatures(droplet, selection.method = "vst", nfeatures = 2000) droplet$stim <- "droplet" ### 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 <- 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 <- NormalizeData(smartseq, verbose = FALSE) smartseq <- FindVariableFeatures(smartseq, selection.method = "vst", nfeatures = 2000) ########## sctransform ###################3 smartseq <- SCTransform(smartseq) ##### Marseq data ###################### raw.data.mars <- read.table("/restricted/projectnb/waxmanlab/kkarri/scRNAseq_data_integration/marseq_hep_expression_backsubtracted.txt", header=T, row.names="ID") plate_metadata <- read.csv("/restricted/projectnb/waxmanlab/kkarri/scRNAseq_data_integration/marseq_annotation.csv", sep=",", header = TRUE) colnames(plate_metadata)[1] <- "plate.barcode" plate.barcodes = lapply(colnames(raw.data.mars), 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.mars) barcode.df= cbind(barcode.df, colnames(raw.data.mars)) 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.mars = raw.data.mars[,rownames(meta.data)] marseq <- CreateSeuratObject(raw.data.mars) marseq <- AddMetaData(object = marseq, meta.data) marseq@meta.data$tech <- "marseq" marseq$stim <- "marseq" #marseq <- SubsetData(marseq,subset.names = c("nGene", "nUMI"), low.thresholds = c(500, 1000)) marseq<- subset(marseq, subset = nFeature_RNA > 500 & nCount_RNA > 1000) marseq <- NormalizeData(marseq, verbose = FALSE) marseq <- FindVariableFeatures(marseq, selection.method = "vst", nfeatures = 2000) ############################ SCtranform option ################################3 marseq <- SCTransform(marseq,verbose = TRUE) ############## marseq endothelial cell data ############################ raw.data.mars1 <- read.table("/restricted/projectnb/waxmanlab/kkarri/scRNAseq_data_integration/EndothelialCells(1073)_cellcount_backgroundUMI.txt", header=T, row.names="ID") plate_metadata <- read.csv("/restricted/projectnb/waxmanlab/kkarri/scRNAseq_data_integration/EndothelialCells_1073_metadata.csv", sep=",", header = TRUE) colnames(plate_metadata)[1] <- "plate.barcode" plate.barcodes = lapply(colnames(raw.data.mars1), 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.mars1) barcode.df= cbind(barcode.df, colnames(raw.data.mars1)) 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.mars1 = raw.data.mars1[,rownames(meta.data)] marseq2 <- CreateSeuratObject(raw.data.mars1) marseq2 <- AddMetaData(object = marseq2, meta.data) marseq2@meta.data$tech <- "marseq2" marseq2$stim <- "marseq2" #marseq_NPC <- SubsetData(marseq,subset.names = c("nGene", "nUMI"), low.thresholds = c(500, 1000)) marseq2 <- subset(marseq2, subset = nFeature_RNA > 200 & nCount_RNA > 500) marseq2 <- NormalizeData(marseq2, verbose = FALSE) marseq2 <- FindVariableFeatures(marseq2, selection.method = "vst", nfeatures = 2000) ############################ SCtranform option ################################3 marseq2 <- SCTransform(marseq2,verbose = TRUE) ##########3 option 1 for scTransform ##################3 combined.features <- SelectIntegrationFeatures(object.list = list(droplet, smartseq,marseq), nfeatures = 2000) combined.list <- PrepSCTIntegration(object.list = list(droplet, smartseq,marseq, marseq2), anchor.features = combined.features, verbose = TRUE) combined.anchors <- FindIntegrationAnchors(object.list = combined.list, normalization.method = "SCT",anchor.features = combined.features, verbose = TRUE) combined.integrated <- IntegrateData(anchorset = combined.anchors, normalization.method = "SCT", verbose = TRUE) combined.integrated <- RunPCA(combined.integrated, verbose = FALSE) combined.integrated <- RunUMAP(combined.integrated, dims = 1:30) plots <- DimPlot(combined.integrated, group.by = c("stim", "mouse.sex"), combine = FALSE) plots <- lapply(X = plots, FUN = function(x) x + theme(legend.position = "top") + guides(color = guide_legend(nrow = 3, byrow = TRUE, override.aes = list(size = 3)))) CombinePlots(plots) #######################3 option 2 standard workflow merging ################################ anchors <- FindIntegrationAnchors(object.list = list(droplet, smartseq,marseq), 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") ``` ```{r} DefaultAssay(combined) <- "RNA" combined <- NormalizeData(combined, verbose = FALSE) # Normalize RNA data for visualization purposes if using sctranform combined.integrated <- NormalizeData(combined.integrated, verbose = FALSE) 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)) NPC <- DotPlot(combined, features = c(genes_endo,genes_kuppfer, genes_nk)) #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") #ranme the clusters based on identity combined <- RenameIdents(combined, `0` = "Hepatocytes Pericentral-F", `1` = "Hepatocytes Pericentral-M", `2` = "Hepatocytes Periportal-M",`3` = "Hepatocytes Midlobular-M", `4` = "Hepatocytes Midlobular-F", `5` = "Hepatocytes Mid-Periportal-M", `6` = "Hepatocytes Periportal-F", `7` = "Hepatocytes Periportal-M", `8` = "Endothelial Midlobular-M", `9` = "Hepatocytes Mid-Periportal-M",`10` = "Hepatocytes Midlobular-M", `11` = "Hepatocytes Midlobular-F", `12`="Kupffer-M") DimPlot(combined, label = TRUE) save(combined, file="Seurat_smart-drop_integrated.Robj") ########### tSNE ################################# combined <- NormalizeData(object = combined) combined <- FindVariableFeatures(combined, selection.method = "vst", nfeatures = 2000) ``` Find differential expression markers ```{r} DefaultAssay(combined) <- "RNA" PC.markers <- FindConservedMarkers(combined, ident.1 = c(0,1,2,3,4,5,6,7),ident.2 = 8, grouping.var = 'stim', verbose = FALSE, thresh.use=0.1, only.pos=TRUE ) head(PC.markers) lnc_pc_markers <- grep(pattern = "^ncRNA", x= rownames(PC.markers), value = TRUE) lnc_pc_markers lnc_markers_table <- PC.markers[lnc_pc_markers,] lnc_markers_table lnc_pc_dot <- DotPlot(combined, features = lnc_pc_markers) lnc_pc_feature <- FeaturePlot(combined, features = lnc_pc_markers, min.cutoff = "q9", reduction = "umap",) ``` ```{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' all_genes = c(genes_hep, genes_endo, genes_kuppfer, genes_nk, genes_b, genes_bec, genes_immune) 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') ``` 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_", tiss1ue_of_interest, "seurat_smartdrop-integrated-8272019.Robj")) load(file=filename) ``` # Export the final metadata ```{r} save_annotation_csv(tiss1, tiss1ue_of_interest, "droplet") ```