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 |
|---|---|---|
| PBMCs |
GSM4725044 GSM4725045 GSM4725046 GSM4725050 GSM4725051 GSM4725052 GSM4725056 GSM4725057 GSM4725058 GSM4725062 GSM4725063 GSM4725064 GSM4725068 GSM4725069 GSM4725070
|
|
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GSM4725041 GSM4725042 GSM4725043 GSM4725047 GSM4725048 GSM4725049 GSM4725053 GSM4725054 GSM4725055 GSM4725059 GSM4725060 GSM4725061 GSM4725065 GSM4725066 GSM4725067
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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.