Raw gene Expression data is sourced from GEO, and the appropriate db package for mapping probes to gene symbols was sourced from the Bioconductor AnnotationData packages.
You can read more about microarray data here.
Select conditions below to toggle them from the plot:
| GROUP | CONDITION | SAMPLES |
|---|---|---|
| Case condition |
GSM4911727 GSM4911729 GSM4911731 GSM4911733 GSM4911735 GSM4911737 GSM4911739
|
|
|
GSM4911728 GSM4911730 GSM4911732 GSM4911734 GSM4911736 GSM4911738 GSM4911740
|
||
| Control condition |
GSM4911741 GSM4911743 GSM4911745 GSM4911747 GSM4911749
|
|
|
GSM4911742 GSM4911744 GSM4911746 GSM4911748 GSM4911750
|
Submission Date: Nov 17, 2020
Summary: We used gene expression microarray to understand the gene expression changes in skeletal muscle one year follow RYGB weight loss surgery.
GEO Accession ID: GSE161643
PMID: No Pubmed ID
Submission Date: Nov 17, 2020
Summary: We used gene expression microarray to understand the gene expression changes in skeletal muscle one year follow RYGB weight loss surgery.
GEO Accession ID: GSE161643
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.