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.
Enter gene symbol:

Select conditions below to toggle them from the plot:

GROUP CONDITION SAMPLES
BAT
GSM4278942 GSM4278943 GSM4278944
GSM4278945 GSM4278946 GSM4278947
GSM4278939 GSM4278940 GSM4278941
GSM4278948 GSM4278949 GSM4278950
GSM4278954 GSM4278955 GSM4278956
GSM4278951 GSM4278952 GSM4278953
Description

Submission Date: Jan 22, 2020

Summary: Uncoupling protein-1 (UCP1) plays a central role in energy dissipation in brown adipose tissue (BAT). Using high-throughput library screening of secreted peptides, we identified two fibroblast growth factors (FGF), FGF6 and FGF9, as novel and potent inducers of UCP1 expression in adipocytes and preadipocytes. Here we show the transcriptome of FGF6-stimulated mouse brown preadipocytes.

GEO Accession ID: GSE144061

PMID: 32184391

Description

Submission Date: Jan 22, 2020

Summary: Uncoupling protein-1 (UCP1) plays a central role in energy dissipation in brown adipose tissue (BAT). Using high-throughput library screening of secreted peptides, we identified two fibroblast growth factors (FGF), FGF6 and FGF9, as novel and potent inducers of UCP1 expression in adipocytes and preadipocytes. Here we show the transcriptome of FGF6-stimulated mouse brown preadipocytes.

GEO Accession ID: GSE144061

PMID: 32184391

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:

Select conditions:

Control Condition

Perturbation Condition

Only conditions with at least 1 replicate are available to select

Differential Gene Expression Analysis
Info Differential expression signatures can be computed using DESeq2 or characteristic direction.
Select differential expression analysis method:
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.