CD40 activation and the effect on Neutrophils

##### CD40 activation and the effect on Neutrophils # Load necessary libraries for data manipulation, analysis, and visualization library(dplyr) library(Seurat) library(patchwork) library(plyr) # Set the working directory to the folder containing the data setwd("C:/Users/ALL/sciebo - Lang, Alexander ([e-mail])@uni-duesseldorf.sciebo.de/ALL_NGS/scRNAseq/scRNAseq/05_FGK45 Wirkung auf Neutros - scRNAseq/938-1_cellranger_count/outs") # Read the M0 dataset from the 10X Genomics format pbmc.data <- Read10X(data.dir = "filtered_feature_bc_matrix/") RNA <- pbmc.data$`Gene Expression` ADT <- pbmc.data$`Antibody Capture` HST <- pbmc.data$`Multiplexing Capture` # Load the Matrix package library(Matrix) # Hashtag 1, 2 and 3 are marking the organs (heart, blood, spleen) # Subset the rows based on row names subsetted_rows <- c("TotalSeq-B0301", "TotalSeq-B0302", "TotalSeq-B0303") animals_data <- HST[subsetted_rows, , drop = FALSE] # Hashtag 4, 5, 6, 7 are representing IgG_1, IgG_1, FGK45_1 and FGK45_1 subsetted_rows <- c("TotalSeq-B0304", "TotalSeq-B0305", "TotalSeq-B0306", "TotalSeq-B0307") treatment_data <- HST[subsetted_rows, , drop = FALSE] #Create a Seurat obeject and more assays to combine later RNA <- CreateSeuratObject(counts = RNA) ADT <- CreateAssayObject(counts = ADT) Organ <- CreateAssayObject(counts = animals_data) Treatment <- CreateAssayObject(counts = treatment_data) seurat <- RNA #Add the Assays seurat[["ADT"]] <- ADT seurat[["HST_Mice"]] <- Organ seurat[["HST_Treatment"]] <- Treatment #Check for AK Names rownames(seurat[["ADT"]]) #Cluster cells on the basis of their scRNA-seq profiles # perform visualization and clustering steps DefaultAssay(seurat) <- "RNA" seurat <- NormalizeData(seurat) seurat <- FindVariableFeatures(seurat) seurat <- ScaleData(seurat) seurat <- RunPCA(seurat, verbose = FALSE) seurat <- FindNeighbors(seurat, dims = 1:30) seurat <- FindClusters(seurat, resolution = 0.8, verbose = FALSE) seurat <- RunUMAP(seurat, dims = 1:30) DimPlot(seurat, label = TRUE) FeaturePlot(seurat, features = "S100a9", order = T) # Normalize ADT data, DefaultAssay(seurat) <- "ADT" seurat <- NormalizeData(seurat, normalization.method = "CLR", margin = 2) #Demultiplex cells based on Mouse_Hashtag Enrichment seurat <- NormalizeData(seurat, assay = "HST_Mice", normalization.method = "CLR") seurat <- HTODemux(seurat, assay = "HST_Mice", positive.quantile = 0.99) #Visualize demultiplexing results # Global classification results table(seurat$HST_Mice_classification.global) DimPlot(seurat, group.by = "HST_Mice_classification") #Demultiplex cells based on Treatment_Hashtag Enrichment seurat <- NormalizeData(seurat, assay = "HST_Treatment", normalization.method = "CLR") seurat <- HTODemux(seurat, assay = "HST_Treatment", positive.quantile = 0.99) #Visualize demultiplexing results # Global classification results table(seurat$HST_Treatment_classification.global) DimPlot(seurat, group.by = "HST_Treatment_classification") Idents(seurat) <- seurat$HST_Treatment_classification pbmc.singlet <- subset(seurat, idents = "Negative", invert = T) Idents(pbmc.singlet) <- pbmc.singlet$HST_Mice_classification pbmc.singlet <- subset(pbmc.singlet, idents = "Negative", invert = T) DimPlot(pbmc.singlet, group.by = "HST_Treatment_maxID") #Redo the clssification to remove the doublettes pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Treatment", positive.quantile = 0.99) table(pbmc.singlet$HST_Treatment_classification.global) DimPlot(pbmc.singlet, group.by = "HST_Treatment_classification") pbmc.singlet <- subset(pbmc.singlet, idents = "Doublet", invert = T) pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Mice", positive.quantile = 0.99) table(pbmc.singlet$HST_Mice_classification.global) pbmc.singlet <- subset(pbmc.singlet, idents = "Doublet", invert = T) pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Mice", positive.quantile = 0.60) pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Treatment", positive.quantile = 0.60) DimPlot(pbmc.singlet, group.by = "HST_Treatment_maxID") DimPlot(pbmc.singlet, group.by = "HST_Mice_maxID") seurat <- pbmc.singlet seurat$organ <- seurat$HST_Mice_maxID seurat$mouse <- seurat$HST_Treatment_maxID seurat$treatment <- seurat$HST_Treatment_maxID library(plyr) seurat$treatment <- revalue(seurat$treatment, c( "TotalSeq-B0304" = "IgG", "TotalSeq-B0305" = "IgG", "TotalSeq-B0306" = "FGK45", "TotalSeq-B0307" = "FGK45" )) library(plyr) seurat$organ <- revalue(seurat$organ, c( "TotalSeq-B0301" = "heart", "TotalSeq-B0302" = "blood", "TotalSeq-B0303" = "spleen" )) seurat$mouse <- revalue(seurat$mouse, c( "TotalSeq-B0304" = "1", "TotalSeq-B0305" = "2", "TotalSeq-B0306" = "3", "TotalSeq-B0307" = "4" )) #Cluster cells on the basis of their scRNA-seq profiles without doublettes # perform visualization and clustering steps DefaultAssay(seurat) <- "RNA" seurat <- NormalizeData(seurat) seurat <- FindVariableFeatures(seurat) seurat <- ScaleData(seurat) seurat <- RunPCA(seurat, verbose = FALSE) seurat <- FindNeighbors(seurat, dims = 1:30) seurat <- FindClusters(seurat, resolution = 0.8, verbose = FALSE) seurat <- RunUMAP(seurat, dims = 1:30) DimPlot(seurat, label = TRUE) DefaultAssay(seurat) <- "ADT" seurat <- NormalizeData(seurat, normalization.method = "CLR", margin = 2) setwd("C:/Users/ALL/sciebo - Lang, Alexander ([e-mail])@uni-duesseldorf.sciebo.de/ALL_NGS/scRNAseq/scRNAseq/05_FGK45 Wirkung auf Neutros - scRNAseq/Analyse") saveRDS(seurat, file = "FGK45_heart_blood_spleen.v0.1.RDS")