Gene counts are sourced from ARCHS4, which provides uniform alignment of GEO samples.
You can learn more about ARCHS4 and its pipeline here.
Select conditions below to toggle them from the plot:
| GROUP | CONDITION | SAMPLES |
|---|---|---|
| kidney |
GSM6429685 GSM6429686 GSM6429689 GSM6429690
|
|
|
GSM6429682 GSM6429683 GSM6429684 GSM6429687 GSM6429688
|
Submission Date: Aug 03, 2022
Summary: To establish the role of proximal tubular hypoxia in diabetic kidney disease, we use a mouse line with a specific deletion of von-Hippel-Lindau (VHL) in the proximal tubule and treat them with streptozotocin (STZ) to induce a type I diabetes mellitus. 10 weeks after induction of diabetes mellitus samples were collected.
GEO Accession ID: GSE210401
PMID: No Pubmed ID
Submission Date: Aug 03, 2022
Summary: To establish the role of proximal tubular hypoxia in diabetic kidney disease, we use a mouse line with a specific deletion of von-Hippel-Lindau (VHL) in the proximal tubule and treat them with streptozotocin (STZ) to induce a type I diabetes mellitus. 10 weeks after induction of diabetes mellitus samples were collected.
GEO Accession ID: GSE210401
PMID: No Pubmed ID
Visualizations are precomputed using the Python package scanpy on the top 5000 most variable genes.
Differential expression signatures are automatically computed using the limma R package.
More options for differential expression are available to compute below.
Signatures:
Control Condition
Perturbation Condition
Only conditions with at least 1 replicate are available to select
Differential expression signatures can be computed using DESeq2 or characteristic direction.
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.