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 |
|---|---|---|
| Human Brain |
GSM4222739 GSM4222740 GSM4222741
|
|
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GSM4222742 GSM4222743 GSM4222744
|
Submission Date: Dec 17, 2019
Summary: Brain microvessels form the blood-brain barrier, and are dysfunctional in several neurological disorders. Brain microvessels are formed by brain microvascular endothelial cells (BMECs) and pericytes, and the molecular constituents of these cell types remain incompletely characterized, especially in humans. To improve molecular knowledge of these cell types and identify species differences in gene expression, we performed RNA-sequencing on brain microvessels isolated from human and mouse tissue samples using laser capture microdissection. We also performed RNA-sequencing of matched whole brain samples to identify genes with microvessel-enriched expression.
GEO Accession ID: GSE142209
PMID: 32704093
Submission Date: Dec 17, 2019
Summary: Brain microvessels form the blood-brain barrier, and are dysfunctional in several neurological disorders. Brain microvessels are formed by brain microvascular endothelial cells (BMECs) and pericytes, and the molecular constituents of these cell types remain incompletely characterized, especially in humans. To improve molecular knowledge of these cell types and identify species differences in gene expression, we performed RNA-sequencing on brain microvessels isolated from human and mouse tissue samples using laser capture microdissection. We also performed RNA-sequencing of matched whole brain samples to identify genes with microvessel-enriched expression.
GEO Accession ID: GSE142209
PMID: 32704093
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.