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 |
|---|---|---|
| Epicardial Adipose Tissue |
GSM2917179 GSM2917180 GSM2917181 GSM2917182 GSM2917183
|
|
|
GSM2917176 GSM2917177 GSM2917178
|
||
| Subcutaneous Adipose Tissue |
GSM2917171 GSM2917172 GSM2917173 GSM2917174 GSM2917175
|
|
|
GSM2917168 GSM2917169 GSM2917170
|
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.