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 |
|---|---|---|
| coldroom (4°c) |
GSM4007543
|
|
| coldroom (4°c) |
GSM4007542 GSM4007550 GSM4007551 GSM4007558
|
|
|
GSM4007546 GSM4007547 GSM4007554 GSM4007555
|
||
| room temp (24°c) |
GSM4007544 GSM4007545 GSM4007552 GSM4007553 GSM4007559
|
|
|
GSM4007548 GSM4007549 GSM4007556 GSM4007557 GSM4007560
|
Submission Date: Aug 05, 2019
Summary: Purpose: We investigated the transcriptomic change in brown fat of young and old mice (wild type) through high-throughput RNA-sequencing (RNA-Seq) analysis when the mice were exposed to cold room or room temperatur.
Methods: We prepared 10 of young (3 months) mice and 9 of old (24 months) mice, and kept them in cold room (4°c) or room temperature (24°c) for 24 hours. Then, we sacrified mice and extracted RNA from brown fat tissue (BAT) for RNA-seq experiment.
Results: BAT of Young mice showed increased carbohydrate metabolism and glycolytic flux during cold exposure
Conclusions: The thermogenesis function of BAT is accelerated on cold exposure.
GEO Accession ID: GSE135391
PMID: 32795388
Submission Date: Aug 05, 2019
Summary: Purpose: We investigated the transcriptomic change in brown fat of young and old mice (wild type) through high-throughput RNA-sequencing (RNA-Seq) analysis when the mice were exposed to cold room or room temperatur.
Methods: We prepared 10 of young (3 months) mice and 9 of old (24 months) mice, and kept them in cold room (4°c) or room temperature (24°c) for 24 hours. Then, we sacrified mice and extracted RNA from brown fat tissue (BAT) for RNA-seq experiment.
Results: BAT of Young mice showed increased carbohydrate metabolism and glycolytic flux during cold exposure
Conclusions: The thermogenesis function of BAT is accelerated on cold exposure.
GEO Accession ID: GSE135391
PMID: 32795388
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.