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 |
|---|---|---|
| Brown Adipose Tissue |
GSM4305742 GSM4305743 GSM4305744
|
|
|
GSM4305739 GSM4305740 GSM4305741
|
Submission Date: Feb 10, 2020
Summary: Next generation sequencing was used to compare transcriptome profiling in brown adipose tissue from Ctrl and BKO mice with mature brown adipose tissue specific deletion of BSCL2. Using an optimized data analysis workflow, we identified 13,525 transcripts in brown adipose tissue. 243 genes were differentially expressed with a fold change ≥1.5 and Padj value <0.05. Only 99 genes showed differential expression between the Ctrl and BKO brown fat, with a fold change ≥2.0 and Padj value <0.05. Results provide insights into the processes and signaling pathways BSCL2-mediated in brown adipose tissue.
GEO Accession ID: GSE145070
PMID: 32246911
Submission Date: Feb 10, 2020
Summary: Next generation sequencing was used to compare transcriptome profiling in brown adipose tissue from Ctrl and BKO mice with mature brown adipose tissue specific deletion of BSCL2. Using an optimized data analysis workflow, we identified 13,525 transcripts in brown adipose tissue. 243 genes were differentially expressed with a fold change ≥1.5 and Padj value <0.05. Only 99 genes showed differential expression between the Ctrl and BKO brown fat, with a fold change ≥2.0 and Padj value <0.05. Results provide insights into the processes and signaling pathways BSCL2-mediated in brown adipose tissue.
GEO Accession ID: GSE145070
PMID: 32246911
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.