Gene Expression Data Explorer
Info Gene counts are sourced from ARCHS4, which provides uniform alignment of GEO samples. You can learn more about ARCHS4 and its pipeline here.
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GROUP CONDITION SAMPLES
coldroom (4°c)
GSM4007543
coldroom (4°c)
GSM4007546 GSM4007547 GSM4007554 GSM4007555
GSM4007542 GSM4007550 GSM4007551 GSM4007558
room temp (24°c)
GSM4007548 GSM4007549 GSM4007556 GSM4007557 GSM4007560
GSM4007544 GSM4007545 GSM4007552 GSM4007553 GSM4007559
Description

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

Description

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

Visualize Samples

Info Visualizations are precomputed using the Python package scanpy on the top 5000 most variable genes.

Precomputed Differential Gene Expression

Info Differential expression signatures are automatically computed using the limma R package. More options for differential expression are available to compute below.

Signatures:

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Control Condition

Perturbation Condition

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Differential Gene Expression Analysis
Info Differential expression signatures can be computed using DESeq2 or characteristic direction.
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Bulk RNA-seq Appyter

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.