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 |
|---|---|---|
| Pancreas |
GSM2136957 GSM2136958 GSM2136965 GSM2136966 GSM2136973 GSM2136974
|
|
|
GSM2136959 GSM2136960 GSM2136967 GSM2136968 GSM2136975 GSM2136976
|
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|
GSM2136961 GSM2136962 GSM2136969 GSM2136970 GSM2136977 GSM2136978
|
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|
GSM2136955 GSM2136956 GSM2136963 GSM2136964 GSM2136971 GSM2136972
|
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.