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
Epididymal White Adipose Tissue (WAT)
GSM4564591 GSM4564592 GSM4564593 GSM4564594
GSM4564587 GSM4564588 GSM4564589 GSM4564590
Description

Submission Date: May 21, 2020

Summary: Mice maintained on high-fat diet (Research Diets #D12492) for 37 weeks.

GEO Accession ID: GSE151030

PMID: 32941798

Description

Submission Date: May 21, 2020

Summary: Mice maintained on high-fat diet (Research Diets #D12492) for 37 weeks.

GEO Accession ID: GSE151030

PMID: 32941798

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.
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.