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Single Gene Queries
Gene Set Queries
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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 |
|---|---|---|
| alveolar bone |
GSM5543286 GSM5543287 GSM5543288 GSM5543289 GSM5543290 GSM5543291 GSM5543292 GSM5543293 GSM5543294 GSM5543295
|
|
|
GSM5543278 GSM5543279 GSM5543280 GSM5543281 GSM5543282 GSM5543283 GSM5543284 GSM5543285
|
Submission Date: Aug 27, 2021
Summary: The objective of this study is to investigate alveolar bone gene expression in health and diabetes through RNA-sequencing and bioinformatics analysis.
GEO Accession ID: GSE182923
PMID: No Pubmed ID
Submission Date: Aug 27, 2021
Summary: The objective of this study is to investigate alveolar bone gene expression in health and diabetes through RNA-sequencing and bioinformatics analysis.
GEO Accession ID: GSE182923
PMID: No Pubmed ID
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 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.
