GSE137631: Effects of exercise training on skeletal muscle insulin sensitivity in obesity patients undergoing RYGB surgery: a randomized controlled trial

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.
Enter gene symbol:

Select conditions below to toggle them from the plot:

GROUP CONDITION SAMPLES
skeletal muscle (vastus lateralis)
GSM4083534 GSM4083536 GSM4083538 GSM4083540 GSM4083542 GSM4083544
GSM4083535 GSM4083537 GSM4083539 GSM4083541 GSM4083543 GSM4083545
GSM4083523 GSM4083525 GSM4083527 GSM4083529 GSM4083531 GSM4083533
GSM4083516 GSM4083517 GSM4083518 GSM4083519 GSM4083520 GSM4083521
GSM4083522 GSM4083524 GSM4083526 GSM4083528 GSM4083530 GSM4083532
Description

Submission Date: Sep 18, 2019

Summary: We reported that skeletal muscle insulin sensitivity was restored to a lean phenotype with exercise training in patients undergoing RYGB surgery. For that, the goals of this study is to identify changes in the skeletal muscle transcriptome profiling (RNA-seq) that could explain the insulin sensitivity improvement of RYGB + exercise training group.

GEO Accession ID: GSE137631

PMID: 32409493

Description

Submission Date: Sep 18, 2019

Summary: We reported that skeletal muscle insulin sensitivity was restored to a lean phenotype with exercise training in patients undergoing RYGB surgery. For that, the goals of this study is to identify changes in the skeletal muscle transcriptome profiling (RNA-seq) that could explain the insulin sensitivity improvement of RYGB + exercise training group.

GEO Accession ID: GSE137631

PMID: 32409493

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:

Select conditions:

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.