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Gene Expression Data Explorer
Gene counts are sourced from ARCHS4, which provides uniform alignment of GEO samples.
You can learn more about ARCHS4 and its pipeline here.
Gene counts are sourced from ARCHS4, which provides uniform alignment of GEO samples.
You can learn more about ARCHS4 and its pipeline here.
Enter gene symbol:
Select conditions below to toggle them from the plot:
| GROUP | CONDITION | SAMPLES |
|---|---|---|
| Chow Diet; Male C57BL/6J mice |
GSM3464441 GSM3464442 GSM3464443 GSM3464444 GSM3464445 GSM3464446
|
|
|
GSM3464436 GSM3464437 GSM3464438 GSM3464439 GSM3464440
|
||
| High-Fat Diet; Male C57BL/6J mice |
GSM3464453 GSM3464454 GSM3464455 GSM3464456 GSM3464457 GSM3464458
|
|
|
GSM3464447 GSM3464448 GSM3464449 GSM3464450 GSM3464451 GSM3464452
|
mouse
GSE122348
Visualize Samples
Visualizations are precomputed using the Python package scanpy on the top 5000 most variable genes.
Precomputed Differential Gene Expression
Differential expression signatures are automatically computed using the limma R package.
More options for differential expression are available to compute below.
Signatures:
Select conditions:
Control Condition
Perturbation Condition
Only conditions with at least 1 replicate are available to select
Differential Gene Expression Analysis
Differential expression signatures can be computed using DESeq2 or characteristic direction.
Differential expression signatures can be computed using DESeq2 or characteristic direction.
Select differential expression analysis method:
Bulk RNA-seq Appyter
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.
