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 |
|---|---|---|
| Liver |
GSM6500652 GSM6500653 GSM6500654
|
|
|
GSM6500649 GSM6500650 GSM6500651
|
||
| White adipose tissue |
GSM6500658 GSM6500659 GSM6500660
|
|
|
GSM6500655 GSM6500656 GSM6500657
|
Submission Date: Aug 22, 2022
Summary: To investigate the effect of miR-503 in aging associated type 2 diabetes, target genes of miR-503 need to be investigated. The global miR-322-503-351 deletion (KO) mouse was constructed, and RNA-seq was then performed on aged mouse liver and white adipose tissue (WAT).
GEO Accession ID: GSE211749
PMID: No Pubmed ID
Submission Date: Aug 22, 2022
Summary: To investigate the effect of miR-503 in aging associated type 2 diabetes, target genes of miR-503 need to be investigated. The global miR-322-503-351 deletion (KO) mouse was constructed, and RNA-seq was then performed on aged mouse liver and white adipose tissue (WAT).
GEO Accession ID: GSE211749
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.