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
epicardial adipose tissue
GSM4009115 GSM4009116 GSM4009117 GSM4009121 GSM4009122 GSM4009124
GSM4009118 GSM4009119 GSM4009120 GSM4009123 GSM4009125 GSM4009126
Description

Submission Date: Aug 06, 2019

Summary: Accumulating studies have suggested that epicardial adipose tissue play an important role in the pathogenesis of atrial fibrillation (AF), but few have characterized the underlying mechanism between their interactions. Recent evidence suggested that bioactive molecules secreted from EAT, including exosomes carrying non-coding RNAs, may modulate atrial remodeling. The aim of the present study was to investigate the expression profile of mRNAs and ncRNAs in EAT with AF.

GEO Accession ID: GSE135445

PMID: No Pubmed ID

Description

Submission Date: Aug 06, 2019

Summary: Accumulating studies have suggested that epicardial adipose tissue play an important role in the pathogenesis of atrial fibrillation (AF), but few have characterized the underlying mechanism between their interactions. Recent evidence suggested that bioactive molecules secreted from EAT, including exosomes carrying non-coding RNAs, may modulate atrial remodeling. The aim of the present study was to investigate the expression profile of mRNAs and ncRNAs in EAT with AF.

GEO Accession ID: GSE135445

PMID: No Pubmed ID

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.