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
GSM2917179 GSM2917180 GSM2917181 GSM2917182 GSM2917183
GSM2917176 GSM2917177 GSM2917178
Subcutaneous Adipose Tissue
GSM2917171 GSM2917172 GSM2917173 GSM2917174 GSM2917175
GSM2917168 GSM2917169 GSM2917170
Description

Submission Date: Jan 09, 2018

Summary: In this study we performed transcriptome sequencing on the Subcutaneous Adipose Tissue and Epicardial Adipose Tissue of both diabetic and nondiabetic human patients.

GEO Accession ID: GSE108971

PMID: 28739185

Description

Submission Date: Jan 09, 2018

Summary: In this study we performed transcriptome sequencing on the Subcutaneous Adipose Tissue and Epicardial Adipose Tissue of both diabetic and nondiabetic human patients.

GEO Accession ID: GSE108971

PMID: 28739185

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.