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TSd5.RDS - CD40 inhibiton in AMI on d5, seq on d7 and d14

##### CD40 inhibiton in AMI on d5, seq on d7 and d14  # Load necessary libraries for data manipulation, analysis, and visualizationlibrary(dplyr)library(Seurat)library(patchwork)library(plyr) # Set the working directory to the folder containing the datasetwd("C:/Users/ALL/sciebo - Lang, Alexander (allan101@uni-duesseldorf.de)@uni-duesseldorf.sciebo.de/ALL_NGS/scRNAseq/scRNAseq/01_TS_d5_paper/03_CD40 inhibition on day 5, seq on day 7 and 14/938-2_cellranger_count/outs") # Read the M0 dataset from the 10X Genomics formatpbmc.data <- Read10X(data.dir = "filtered_feature_bc_matrix/") RNA <- pbmc.data$`Gene Expression` ADT <- pbmc.data$`Antibody Capture` HST <- pbmc.data$`Multiplexing Capture`  # Load the Matrix packagelibrary(Matrix) # Hashtag 1, 2 and 3 are marking the mouse replicates per condition# Subset the rows based on row namessubsetted_rows <- c("TotalSeq-B0301", "TotalSeq-B0302", "TotalSeq-B0303")animals_data <- HST[subsetted_rows, , drop = FALSE] # Hashtag 4, 5, 6, 7 are representing DMSO d7, TS d7, DMSO d14 and TS d14subsetted_rows <- c(""TotalSeq-B0304", "TotalSeq-B0305", "TotalSeq-B0306", "TotalSeq-B0307")treatment_data <- HST[subsetted_rows, , drop = FALSE] #Create a Seurat obeject and more assays to combine laterRNA <- CreateSeuratObject(counts = RNA)ADT <- CreateAssayObject(counts = ADT)Mice <- CreateAssayObject(counts = animals_data)Treatment <- CreateAssayObject(counts = treatment_data)  seurat <- RNA #Add the Assaysseurat[["ADT"]] <- ADT seurat[["HST_Mice"]] <- Mice seurat[["HST_Treatment"]] <- Treatment #Check for AK Namesrownames(seurat[["ADT"]]) #Cluster cells on the basis of their scRNA-seq profiles# perform visualization and clustering stepsDefaultAssay(seurat) <- "RNA"seurat <- NormalizeData(seurat)seurat <- FindVariableFeatures(seurat)seurat <- ScaleData(seurat)seurat <- RunPCA(seurat, verbose = FALSE)seurat <- FindNeighbors(seurat, dims = 1:30)seurat <- FindClusters(seurat, resolution = 0.8, verbose = FALSE)seurat <- RunUMAP(seurat, dims = 1:30)DimPlot(seurat, label = TRUE) FeaturePlot(seurat, features = "Col1a1", order = T) # Normalize ADT data,DefaultAssay(seurat) <- "ADT"seurat <- NormalizeData(seurat, normalization.method = "CLR", margin = 2) #Demultiplex cells based on Mouse_Hashtag Enrichmentseurat <- NormalizeData(seurat, assay = "HST_Mice", normalization.method = "CLR")seurat <- HTODemux(seurat, assay = "HST_Mice", positive.quantile = 0.60)  #Visualize demultiplexing results# Global classification resultstable(seurat$HST_Mice_classification.global) DimPlot(seurat, group.by = "HST_Mice_classification")  #Demultiplex cells based on Treatment_Hashtag Enrichmentseurat <- NormalizeData(seurat, assay = "HST_Treatment", normalization.method = "CLR")seurat <- HTODemux(seurat, assay = "HST_Treatment", positive.quantile = 0.60)  #Visualize demultiplexing results# Global classification resultstable(seurat$HST_Treatment_classification.global) DimPlot(seurat, group.by = "HST_Treatment_classification") Idents(seurat) <- seurat$HST_Treatment_classificationpbmc.singlet <- subset(seurat, idents = "Negative", invert = T)Idents(pbmc.singlet) <- pbmc.singlet$HST_Mice_classificationpbmc.singlet <- subset(pbmc.singlet, idents = "Negative", invert = T) DimPlot(pbmc.singlet, group.by = "HST_Treatment_maxID") #Redo the clssification to remove the doublettespbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Treatment", positive.quantile = 0.99)table(pbmc.singlet$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$HST_Mice_classification.global)pbmc.singlet <- subset(pbmc.singlet, idents = "Doublet", invert = T) pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Mice", positive.quantile = 0.60)pbmc.singlet <- HTODemux(pbmc.singlet, assay = "HST_Treatment", positive.quantile = 0.60)DimPlot(pbmc.singlet, group.by = "HST_Treatment_maxID")DimPlot(pbmc.singlet, group.by = "HST_Mice_maxID")  seurat <- pbmc.singlet seurat$mice <- seurat$HST_Mice_maxIDseurat$treatment <- seurat$HST_Treatment_maxID  library(plyr)seurat$treatment <- revalue(seurat$treatment, c( "TotalSeq-B0304" = "DMSO_d7", "TotalSeq-B0305" = "TS_d7", "TotalSeq-B0306" = "DMSO_d14", "TotalSeq-B0307" = "TS_d14")) library(plyr)seurat$mice <- revalue(seurat$mice, c( "TotalSeq-B0301" = "1", "TotalSeq-B0302" = "2", "TotalSeq-B0303" = "3"))  #Cluster cells on the basis of their scRNA-seq profiles without doublettes# perform visualization and clustering stepsDefaultAssay(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 (allan101@uni-duesseldorf.de)@uni-duesseldorf.sciebo.de/ALL_NGS/scRNAseq/scRNAseq/01_TS_d5_paper/03_CD40 inhibition on day 5, seq on day 7 and 14/Analyse") saveRDS(seurat, file= "TSd5.v0.1.RDS")

ShareScore

16/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship: 4
Harmonization: 4
Access: 8
Reuse readiness: 0
Engagement: 0

Dataset details

Repository: zenodo
Published: October 16, 2023
License: cc-by-4.0
DOI: 10.5281/zenodo.10015470

Scored with release 0.9.0

TSd5.RDS - CD40 inhibiton in AMI on d5, seq on d7 and d14

Score breakdown

Dataset details

Your academic CV and NIH biosketch