Gene Expression Data Explorer
Info Gene counts are sourced from ARCHS4, which provides uniform alignment of GEO samples. You can learn more about ARCHS4 and its pipeline here.
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GROUP CONDITION SAMPLES
iPSC line NC8
GSM2373097 GSM2373099
GSM2373096 GSM2373098
Description

Submission Date: Nov 02, 2016

Summary: Diabetes is prevalent worldwide and associated with severe health complications, including blood vessel damage that leads to cardiovascular disease and death. We report the development of 3D blood vessel organoids from human embryonic and induced pluripotent stem cells. These human blood vessel organoids contain endothelium, perivascular pericytes, and basal membranes, and self-assemble into lumenized interconnected capillary networks. We treat these vascular organoids with hyperglycemia and inflammatory cytokines in vitro, which leads to basement membrane thickening, a structural hallmark of diabetic patient. To compare differential gene expression we performed RNAseq on endothelial cells, derived from control (NG) or diabetic (DI) vascular organoids.

GEO Accession ID: GSE89475

PMID: 30651639

Description

Submission Date: Nov 02, 2016

Summary: Diabetes is prevalent worldwide and associated with severe health complications, including blood vessel damage that leads to cardiovascular disease and death. We report the development of 3D blood vessel organoids from human embryonic and induced pluripotent stem cells. These human blood vessel organoids contain endothelium, perivascular pericytes, and basal membranes, and self-assemble into lumenized interconnected capillary networks. We treat these vascular organoids with hyperglycemia and inflammatory cytokines in vitro, which leads to basement membrane thickening, a structural hallmark of diabetic patient. To compare differential gene expression we performed RNAseq on endothelial cells, derived from control (NG) or diabetic (DI) vascular organoids.

GEO Accession ID: GSE89475

PMID: 30651639

Visualize Samples

Info Visualizations are precomputed using the Python package scanpy on the top 5000 most variable genes.

Precomputed Differential Gene Expression

Info Differential expression signatures are automatically computed using the limma R package. More options for differential expression are available to compute below.

Signatures:

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Control Condition

Perturbation Condition

Only conditions with at least 1 replicate are available to select

Differential Gene Expression Analysis
Info Differential expression signatures can be computed using DESeq2 or characteristic direction.
Select differential expression analysis method:
Bulk RNA-seq Appyter

This pipeline enables you to analyze and visualize your bulk RNA sequencing datasets with an array of downstream analysis and visualization tools. The pipeline includes: PCA analysis, Clustergrammer interactive heatmap, library size analysis, differential gene expression analysis, enrichment analysis, and L1000 small molecule search.