Cite-Seq mit Aorten und Myokardinfarkt - Analysen

#R Skript

library(Seurat)
library(ggplot2)
library(patchwork)

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`)

cbmc <- rna

cbmc[["ADT"]] <- adt_assay

cbmc[["HST"]] <- multiplex

Assays(cbmc)

rownames(cbmc[["ADT"]])

#perform visualization and clustering steps
cbmc <- NormalizeData(cbmc)
cbmc <- FindVariableFeatures(cbmc)
cbmc <- ScaleData(cbmc)
cbmc <- RunPCA(cbmc, verbose = FALSE)
cbmc <- FindNeighbors(cbmc, dims = 1:30)
cbmc <- FindClusters(cbmc, resolution = 0.8, verbose = FALSE)
cbmc <- RunUMAP(cbmc, dims = 1:30)
DimPlot(cbmc, label = TRUE)

#Normalize ADT data,
DefaultAssay(cbmc) <- "ADT"
cbmc <- NormalizeData(cbmc, normalization.method = "CLR", margin = 2)
DefaultAssay(cbmc) <- "RNA"

#Now, we will visualize CD8a levels for RNA and protein By setting the default assay, we can
#visualize one or the other
DefaultAssay(cbmc) <- "ADT"
p1 <- FeaturePlot(cbmc, "Ms.CD8a", cols = c("lightgrey", "darkgreen")) + ggtitle("CD8a protein")
DefaultAssay(cbmc) <- "RNA"
p2 <- FeaturePlot(cbmc, "Cd8a") + ggtitle("CD8a RNA")

#place plots side-by-side
p1 | p2

#Now, we will visualize CD40 levels for RNA and protein By setting the default assay, we can
#visualize one or the other
DefaultAssay(cbmc) <- "ADT"
p1 <- FeaturePlot(cbmc, "Ms.CD40", cols = c("lightgrey", "darkgreen")) + ggtitle("CD40 protein")
DefaultAssay(cbmc) <- "RNA"
p2 <- FeaturePlot(cbmc, "Cd40") + ggtitle("CD40 RNA")

#place plots side-by-side
p1 | p2

setwd("E:/BMFZ Data/671/Analyse_ALL")
saveRDS(cbmc, file = "Cite_seq_raw.rds")