zenodorestricted

scRNAseq_Dataset Merge AMI d5 (CD45+Fibroblast) + AAA Kinetik + Cite-Seq_Dataset AG Gerdes

Integration Skript:   library(Seurat) library(tidyverse) library(Matrix) #cite <- readRDS("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Merge AAA mit Cite AAA/Cite_seq_v0.41.rds") #CD45 <- readRDS("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Schrader/Fertige_Analysen/TS_d5_paper/CD45.rds") AAA <- readRDS("C:/Users/alex/sciebo/AAA_Zhao_v4.rds") cite <- readRDS("C:/Users/alex/sciebo/CITE_Seq_v0.5.rds") all4 <- readRDS("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Schrader/Fertige_Analysen/Schrader_All4_Rohanalyse/all4_220228.rds") #fuse lists c <- list(cite, all4, AAA) names(c) <- c("cite", "all4", "AAA") pancreas.list <- c[c("cite", "all4", "AAA")] for (i in 1:length(pancreas.list)) {   pancreas.list[[i]] <- SCTransform(pancreas.list[[i]], verbose = FALSE) } pancreas.features <- SelectIntegrationFeatures(object.list = pancreas.list, nfeatures = 3000) #options(future.globals.maxSize= 6091289600) #pancreas.list <- PrepSCTIntegration(object.list = pancreas.list, anchor.features = pancreas.features,                                     #verbose = FALSE) #future.globals.maxsize was to low. changed it to options(future.globals.maxSize= 1091289600) #identify anchors #alternative from tutorial (https://satijalab.org/seurat/articles/integration_introduction.html) #memory.limit(9999999999) features <- SelectIntegrationFeatures(object.list = pancreas.list, nfeatures = 3000) pancreas.list <- PrepSCTIntegration(object.list = pancreas.list, anchor.features = features) pancreas.anchors <- FindIntegrationAnchors(object.list = pancreas.list, normalization.method = "SCT", anchor.features = pancreas.features, verbose = FALSE) pancreas.integrated <- IntegrateData(anchorset = pancreas.anchors, normalization.method = "SCT",                                      verbose = FALSE)   setwd("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Schrader/Fertige_Analysen/TS_d5_paper") saveRDS(pancreas.integrated, file = "integrated_AAA_Cite_AMI.rds") saveRDS(cd45, file = "integrated_AAA_Cite_CD45.rds") seurat <- pancreas.integrated #seurat <- readRDS("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Schrader/Fertige_Analysen/TS_d5_paper/integrated_d5_cite.rds") DefaultAssay(object = seurat) <- "integrated" seurat <- FindVariableFeatures(seurat, selection.method = "vst", nfeatures = 3000) seurat <- ScaleData(seurat, verbose = FALSE) seurat <- RunPCA(seurat, npcs = 30, verbose = FALSE) seurat <- FindNeighbors(seurat, dims = 1:30) seurat <- FindClusters(seurat, resolution = 0.5) seurat <- RunUMAP(seurat, reduction = "pca", dims = 1:30) DimPlot(seurat, reduction = "umap", split.by = "treatment") + NoLegend() DimPlot(seurat, label = T, repel = T) + NoLegend() DefaultAssay(object = seurat) <- "ADT" adt_marker_integrated <- FindAllMarkers(seurat, logfc.threshold = 0.3) write.csv(adt_marker_integrated, file = "adt_marker_all4_integrated.csv") DefaultAssay(object = seurat) <- "RNA" RNA_marker_integrated <- FindAllMarkers(seurat, logfc.threshold = 0.5) write.csv(RNA_marker_integrated, file = "RNA_marker_all4_integrated.csv") DimPlot(seurat, label = T, repel = T, split.by = "tissue") + NoLegend() FeaturePlot(seurat, features = "Cd40", order = T, label = T) FeaturePlot(seurat, features = "Ms.CD40", order = T, label = T) ##### #leanup: > seurat@meta.data[["sen_score1"]] <- NULL > seurat@meta.data[["sen_score2"]] <- NULL > seurat@meta.data[["sen_score3"]] <- NULL > seurat@meta.data[["sen_score4"]] <- NULL > seurat@meta.data[["sen_score5"]] <- NULL > seurat@meta.data[["sen_score6"]] <- NULL > seurat@meta.data[["sen_score7"]] <- NULL > seurat@meta.data[["pANN_0.25_0.1_1211"]]  <- NULL > seurat@meta.data[["DF.classifications_0.25_0.1_1211"]] <- NULL > seurat@meta.data[["DF.classifications_0.25_0.1_466"]] <- NULL > seurat@assays[["prediction.score.celltype"]] <- NULL > seurat@meta.data[["predicted.celltype"]] <- NULL > seurat@meta.data[["DF.classifications_0.25_0.1_184"]] <- NULL > seurat@meta.data[["DF.classifications_0.25_0.1_953"]] <- NULL > seurat@meta.data[["integrated_snn_res.3"]] <- NULL > seurat@meta.data[["RNA_snn_res.3"]] <- NULL > seurat@meta.data[["SingleR"]] <- NULL > seurat@meta.data[["SingleR_fine"]] <- NULL > seurat@meta.data[["ImmGen"]] <- NULL > seurat@meta.data[["ImmGen_fine"]] <- NULL > seurat@meta.data[["percent.mt"]] <- NULL > seurat@meta.data[["nCount_integrated"]] <- NULL > seurat@meta.data[["nFeature_integrated"]] <- NULL > seurat@meta.data[["S.Score"]] <- NULL > seurat@meta.data[["G2M.Score"]] <- NULL > seurat@meta.data[["Phase"]] <- NULL > seurat@meta.data[["sen_score8"]] <- NULL > seurat@meta.data[["sen_score9"]] <- NULL > seurat@meta.data[["sen_score10"]] <- NULL > seurat@meta.data[["sen_score11"]] <- NULL > seurat@meta.data[["sen_score12"]] <- NULL > seurat@meta.data[["sen_score13"]] <- NULL > seurat@meta.data[["sen_score14"]] <- NULL > seurat@meta.data[["sen_score15"]] <- NULL > seurat@meta.data[["sen_score16"]] <- NULL > seurat@meta.data[["sen_score17"]] <- NULL > seurat@meta.data[["sen_score18"]] <- NULL > seurat@meta.data[["sen_score19"]] <- NULL seurat@meta.data[["pANN_0.25_0.1_184"]] <- NULL seurat@meta.data[["pANN_0.25_0.1_953"]] <- NULL seurat@meta.data[["pANN_0.25_0.1_466"]] <- NULL

ShareScore

8/100

Overall dataset sharing score

Score breakdown

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

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