DOI: 10.5281/zenodo.7774809
title: scRNAseq_Dataset Merge AMI d5 (CD45+Fibroblast) + AAA Kinetik + Cite-Seq_Dataset AG Gerdes

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