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
Brown Adipose Tissue
GSM3690828 GSM3690829 GSM3690830
GSM3690825 GSM3690826 GSM3690827
Description

Submission Date: Mar 29, 2019

Summary: We report the RNA expression of the mature brown fat from 6 week old wild type (WT) and PHOSPHO1 knockout (KO) mice. Mature brown fat was isolated from brown adipose tissue after collagenase digestion. Increased expression of mitochondrial genes is found in KO brown fat.

GEO Accession ID: GSE129020

PMID: 32554489

Description

Submission Date: Mar 29, 2019

Summary: We report the RNA expression of the mature brown fat from 6 week old wild type (WT) and PHOSPHO1 knockout (KO) mice. Mature brown fat was isolated from brown adipose tissue after collagenase digestion. Increased expression of mitochondrial genes is found in KO brown fat.

GEO Accession ID: GSE129020

PMID: 32554489

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

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.
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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.