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ShareScore release 0.9.0
Dataset results
182 results for “aaa”
Laparoscopic Versus Open Abdominal Aortic Aneurysm (AAA) Exclusion
ClinicalTrials.gov study NCT00821145. IPD Sharing: Not stated. Countries: 0. Publications: 0.
EndoSure Sensor for Long-term Follow-up After Endovascular AAA Repair
ClinicalTrials.gov study NCT00831870. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Active transcription and Orc1 drive chromatin association of the AAA+ ATPase Pch2 during meiotic G2/prophase
GEO Series GSE144836. Saccharomyces cerevisiae. 16 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Single cell RNAseq analysis of mouse AAA sample
GEO Series GSE141732. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
High throughput sequencing of the small RNAs from banana cv. Grand Naine (AAA group)
GEO Series GSE65727. Musa acuminata AAA Group. 1 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Expression profiling of mRNAs and long noncoding RNAs in mouse Abdominal Aortic Aneurysm (AAA)
GEO Series GSE63396. Mus musculus. 9 samples. Type: Expression profiling by array; Non-coding RNA profiling by array.
RNA-sequence analysis of mouse AAA samples obtained from wild type mice and PSGL-1-/- mice induced by DOCA plus salt or not
GEO Series GSE153425. Mus musculus. 13 samples. Type: Expression profiling by high throughput sequencing.
Active transcription and Orc1 drive chromatin association of the AAA+ ATPase Pch2 during meiotic G2/prophase [RNA-Seq]
GEO Series GSE144835. Saccharomyces cerevisiae. 4 samples. Type: Expression profiling by high throughput sequencing.
Comparative transcriptome analysis of banana (Musa acuminata L. AAA group, cv. Cavendish) leaves and roots in response to low-potassium stress
GEO Series GSE102968. Musa acuminata AAA Group. 4 samples. Type: Expression profiling by high throughput sequencing.
Atherosclerotic process regulates arterial damage, abdominal aortic aneurysm (AAA) formation and rupture
GEO Series GSE224587. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
An intersegmental single-cell profile reveals aortic heterogeneity [AAA]
GEO Series GSE191226. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
aaa
<p>test test</p>
aaa
Open the record for dataset details and reuse information.
AAA
<p>AAAA</p>
scRNAseq_Dataset Merge AMI d5 (CD45+Fibroblast) + AAA Kinetik + Cite-Seq_Dataset AG Gerdes
<p>Integration Skript:</p> <p> </p> <p>library(Seurat)<br> library(tidyverse)<br> library(Matrix)</p> <p>#cite <- readRDS("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Merge AAA mit Cite AAA/Cite_seq_v0.41.rds")<br> #CD45 <- readRDS("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Schrader/Fertige_Analysen/TS_d5_paper/CD45.rds")<br> AAA <- readRDS("C:/Users/alex/sciebo/AAA_Zhao_v4.rds")<br> cite <- readRDS("C:/Users/alex/sciebo/CITE_Seq_v0.5.rds")<br> all4 <- readRDS("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Schrader/Fertige_Analysen/Schrader_All4_Rohanalyse/all4_220228.rds")</p> <p>#fuse lists<br> c <- list(cite, all4, AAA)<br> names(c) <- c("cite", "all4", "AAA")</p> <p>pancreas.list <- c[c("cite", "all4", "AAA")]<br> for (i in 1:length(pancreas.list)) {<br> pancreas.list[[i]] <- SCTransform(pancreas.list[[i]], verbose = FALSE)<br> }</p> <p>pancreas.features <- SelectIntegrationFeatures(object.list = pancreas.list, nfeatures = 3000)<br> #options(future.globals.maxSize= 6091289600)<br> #pancreas.list <- 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</p> <p>#alternative from tutorial (https://satijalab.org/seurat/articles/integration_introduction.html)<br> #memory.limit(9999999999)<br> features <- SelectIntegrationFeatures(object.list = pancreas.list, nfeatures = 3000)<br> pancreas.list <- PrepSCTIntegration(object.list = pancreas.list, anchor.features = features)<br> pancreas.anchors <- FindIntegrationAnchors(object.list = pancreas.list, normalization.method = "SCT", anchor.features = pancreas.features, verbose = FALSE)<br> pancreas.integrated <- IntegrateData(anchorset = pancreas.anchors, normalization.method = "SCT",<br> verbose = FALSE)</p> <p> </p> <p>setwd("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Schrader/Fertige_Analysen/TS_d5_paper")</p> <p>saveRDS(pancreas.integrated, file = "integrated_AAA_Cite_AMI.rds")</p> <p>saveRDS(cd45, file = "integrated_AAA_Cite_CD45.rds")</p> <p>seurat <- pancreas.integrated</p> <p>#seurat <- readRDS("C:/Users/alex/sciebo/ALL_NGS/scRNAseq/scRNAseq/Schrader/Fertige_Analysen/TS_d5_paper/integrated_d5_cite.rds")</p> <p>DefaultAssay(object = seurat) <- "integrated"<br> seurat <- FindVariableFeatures(seurat, selection.method = "vst", nfeatures = 3000)<br> seurat <- ScaleData(seurat, verbose = FALSE)<br> seurat <- RunPCA(seurat, npcs = 30, verbose = FALSE)<br> seurat <- FindNeighbors(seurat, dims = 1:30)<br> seurat <- FindClusters(seurat, resolution = 0.5)<br> seurat <- RunUMAP(seurat, reduction = "pca", dims = 1:30)<br> DimPlot(seurat, reduction = "umap", split.by = "treatment") + NoLegend()</p> <p><br> DimPlot(seurat, label = T, repel = T) + NoLegend()</p> <p>DefaultAssay(object = seurat) <- "ADT"<br> adt_marker_integrated <- FindAllMarkers(seurat, logfc.threshold = 0.3)<br> write.csv(adt_marker_integrated, file = "adt_marker_all4_integrated.csv")</p> <p>DefaultAssay(object = seurat) <- "RNA"<br> RNA_marker_integrated <- FindAllMarkers(seurat, logfc.threshold = 0.5)<br> write.csv(RNA_marker_integrated, file = "RNA_marker_all4_integrated.csv")</p> <p>DimPlot(seurat, label = T, repel = T, split.by = "tissue") + NoLegend()</p> <p>FeaturePlot(seurat, features = "Cd40", order = T, label = T)<br> FeaturePlot(seurat, features = "Ms.CD40", order = T, label = T)</p> <p><br> #####<br> #leanup:<br> > seurat@meta.data[["sen_score1"]] <- NULL<br> > seurat@meta.data[["sen_score2"]] <- NULL<br> > seurat@meta.data[["sen_score3"]] <- NULL<br> > seurat@meta.data[["sen_score4"]] <- NULL<br> > seurat@meta.data[["sen_score5"]] <- NULL<br> > seurat@meta.data[["sen_score6"]] <- NULL<br> > seurat@meta.data[["sen_score7"]] <- NULL<br> > seurat@meta.data[["pANN_0.25_0.1_1211"]] <- NULL<br> > seurat@meta.data[["DF.classifications_0.25_0.1_1211"]] <- NULL<br> > seurat@meta.data[["DF.classifications_0.25_0.1_466"]] <- NULL<br> > seurat@assays[["prediction.score.celltype"]] <- NULL<br> > seurat@meta.data[["predicted.celltype"]] <- NULL<br> > seurat@meta.data[["DF.classifications_0.25_0.1_184"]] <- NULL<br> > seurat@meta.data[["DF.classifications_0.25_0.1_953"]] <- NULL<br> > seurat@meta.data[["integrated_snn_res.3"]] <- NULL<br> > seurat@meta.data[["RNA_snn_res.3"]] <- NULL<br> > seurat@meta.data[["SingleR"]] <- NULL<br> > seurat@meta.data[["SingleR_fine"]] <- NULL<br> > seurat@meta.data[["ImmGen"]] <- NULL<br> > seurat@meta.data[["ImmGen_fine"]] <- NULL<br> > seurat@meta.data[["percent.mt"]] <- NULL<br> > seurat@meta.data[["nCount_integrated"]] <- NULL<br> > seurat@meta.data[["nFeature_integrated"]] <- NULL<br> > seurat@meta.data[["S.Score"]] <- NULL<br> > seurat@meta.data[["G2M.Score"]] <- NULL<br> > seurat@meta.data[["Phase"]] <- NULL<br> > seurat@meta.data[["sen_score8"]] <- NULL<br> > seurat@meta.data[["sen_score9"]] <- NULL<br> > seurat@meta.data[["sen_score10"]] <- NULL<br> > seurat@meta.data[["sen_score11"]] <- NULL<br> > seurat@meta.data[["sen_score12"]] <- NULL<br> > seurat@meta.data[["sen_score13"]] <- NULL<br> > seurat@meta.data[["sen_score14"]] <- NULL<br> > seurat@meta.data[["sen_score15"]] <- NULL<br> > seurat@meta.data[["sen_score16"]] <- NULL<br> > seurat@meta.data[["sen_score17"]] <- NULL<br> > seurat@meta.data[["sen_score18"]] <- NULL<br> > seurat@meta.data[["sen_score19"]] <- NULL<br> seurat@meta.data[["pANN_0.25_0.1_184"]] <- NULL<br> seurat@meta.data[["pANN_0.25_0.1_953"]] <- NULL<br> seurat@meta.data[["pANN_0.25_0.1_466"]] <- NULL</p> <p> </p> <p> </p>
doi_dedup___::cac985e57b9315e211f64b287e6242a7
Open the record for dataset details and reuse information.
AAA_IR_Int_v1
<p>Integration of AAA and MI Data into one file</p> <p> </p>
aaa
<p>a</p>
aaa
<p>a</p>
aaa
<p>aaaaaaaa</p>
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DANDI Archive for NWB datasets
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The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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