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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 |
|---|---|---|
| Vγ9+Vδ2+ T cells |
GSM5005452 GSM5005453 GSM5005454 GSM5005455 GSM5005456
|
|
|
GSM5005457 GSM5005458 GSM5005459 GSM5005460 GSM5005461
|
human
GSE164266
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.
