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
Adipose tissue
GSM3583313 GSM3583323 GSM3583333
GSM3583320 GSM3583321 GSM3583330 GSM3583331 GSM3583340 GSM3583341
GSM3583314 GSM3583324 GSM3583334
GSM3583315 GSM3583317 GSM3583325 GSM3583327 GSM3583335 GSM3583337
GSM3583312 GSM3583322 GSM3583332
GSM3583316 GSM3583326 GSM3583336
GSM3583318 GSM3583328 GSM3583338
GSM3583319 GSM3583329 GSM3583339
Description

Submission Date: Jan 29, 2019

Summary: The primary objective of the study was to investigate the uncoupling protein-1 (UCP1) associated features of human epicardial adipose tissue (eAT) using next generation deep sequencing. In addition, paired mediastinal adipose tissue (mAT) and subcutaneous adipose tissue (sAT) samples colleced from patients undergoing cardic surgeries at our center were included in the study.

GEO Accession ID: GSE125856

PMID: 30996144

Description

Submission Date: Jan 29, 2019

Summary: The primary objective of the study was to investigate the uncoupling protein-1 (UCP1) associated features of human epicardial adipose tissue (eAT) using next generation deep sequencing. In addition, paired mediastinal adipose tissue (mAT) and subcutaneous adipose tissue (sAT) samples colleced from patients undergoing cardic surgeries at our center were included in the study.

GEO Accession ID: GSE125856

PMID: 30996144

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.