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
gluteal adipose progenitor cells
GSM4495690 GSM4495692 GSM4495694 GSM4495696 GSM4495698 GSM4495700
GSM4495689 GSM4495691 GSM4495693 GSM4495695 GSM4495697 GSM4495699
subcutaneous abdominal adipose progenitor cells
GSM4495678 GSM4495680 GSM4495682 GSM4495684 GSM4495686 GSM4495688
GSM4495677 GSM4495679 GSM4495681 GSM4495683 GSM4495685 GSM4495687
Description

Submission Date: Apr 24, 2020

Summary: GWAS studies and our own work have identified RSPO3 as a gene modulating human body fat distribution. The GWAS signal at RSPO3 is coincident with an eQTL in mature adipocytes. To assess the effects of RSPO3 on abdominal and gluteal adipocyte biology, we undertook inducible RSPO3-knockdown in in vitro differentiated immortalized human abdominal and gluteal adipocyte cell lines (DFAT cells).

GEO Accession ID: GSE149294

PMID: 32493999

Description

Submission Date: Apr 24, 2020

Summary: GWAS studies and our own work have identified RSPO3 as a gene modulating human body fat distribution. The GWAS signal at RSPO3 is coincident with an eQTL in mature adipocytes. To assess the effects of RSPO3 on abdominal and gluteal adipocyte biology, we undertook inducible RSPO3-knockdown in in vitro differentiated immortalized human abdominal and gluteal adipocyte cell lines (DFAT cells).

GEO Accession ID: GSE149294

PMID: 32493999

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.