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 |
|---|---|---|
| pancreatic islets |
GSM4838434 GSM4838437
|
|
|
GSM4838433 GSM4838436
|
||
|
GSM4838435 GSM4838438
|
Submission Date: Oct 20, 2020
Summary: We performed RNA-sequencing in uninfected, SARS-CoV-2-infected, and additionally remdesivir treated ex vivo cultured human islets from two donors to shed light on the transcriptional changes occurring upon viral infection.
GEO Accession ID: GSE159717
PMID: 33536639
Submission Date: Oct 20, 2020
Summary: We performed RNA-sequencing in uninfected, SARS-CoV-2-infected, and additionally remdesivir treated ex vivo cultured human islets from two donors to shed light on the transcriptional changes occurring upon viral infection.
GEO Accession ID: GSE159717
PMID: 33536639
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.