Cite-Seq mit Aorten und Myokardinfarkt - Analysen
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#R Skript library(Seurat)<br> library(ggplot2)<br> library(patchwork) <br> setwd("E:/BMFZ Data/671/671-1_cellranger_count/outs") data <- Read10X(data.dir ="filtered_feature_bc_matrix/") rna <- CreateSeuratObject(counts = data$`Gene Expression`) adt_assay <- CreateAssayObject(counts = data$`Antibody Capture`) multiplex <- CreateAssayObject(counts = data$`Multiplexing Capture`) <br> cbmc <- rna cbmc[["ADT"]] <- adt_assay cbmc[["HST"]] <- multiplex Assays(cbmc) rownames(cbmc[["ADT"]]) #perform visualization and clustering steps<br> cbmc <- NormalizeData(cbmc)<br> cbmc <- FindVariableFeatures(cbmc)<br> cbmc <- ScaleData(cbmc)<br> cbmc <- RunPCA(cbmc, verbose = FALSE)<br> cbmc <- FindNeighbors(cbmc, dims = 1:30)<br> cbmc <- FindClusters(cbmc, resolution = 0.8, verbose = FALSE)<br> cbmc <- RunUMAP(cbmc, dims = 1:30)<br> DimPlot(cbmc, label = TRUE) <br> #Normalize ADT data,<br> DefaultAssay(cbmc) <- "ADT"<br> cbmc <- NormalizeData(cbmc, normalization.method = "CLR", margin = 2)<br> DefaultAssay(cbmc) <- "RNA" #Now, we will visualize CD8a levels for RNA and protein By setting the default assay, we can<br> #visualize one or the other<br> DefaultAssay(cbmc) <- "ADT"<br> p1 <- FeaturePlot(cbmc, "Ms.CD8a", cols = c("lightgrey", "darkgreen")) + ggtitle("CD8a protein")<br> DefaultAssay(cbmc) <- "RNA"<br> p2 <- FeaturePlot(cbmc, "Cd8a") + ggtitle("CD8a RNA") #place plots side-by-side<br> p1 | p2 <br> #Now, we will visualize CD40 levels for RNA and protein By setting the default assay, we can<br> #visualize one or the other<br> DefaultAssay(cbmc) <- "ADT"<br> p1 <- FeaturePlot(cbmc, "Ms.CD40", cols = c("lightgrey", "darkgreen")) + ggtitle("CD40 protein")<br> DefaultAssay(cbmc) <- "RNA"<br> p2 <- FeaturePlot(cbmc, "Cd40") + ggtitle("CD40 RNA") #place plots side-by-side<br> p1 | p2 setwd("E:/BMFZ Data/671/Analyse_ALL")<br> saveRDS(cbmc, file = "Cite_seq_raw.rds")
