GSE159612: Obesity in cardiac surgery patients

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
myocardium (atrial biopsy)
GSM4835833 GSM4835834 GSM4835836 GSM4835842 GSM4835848 GSM4835851 GSM4835857 GSM4835860 GSM4835864 GSM4835865
GSM4835818 GSM4835829 GSM4835839 GSM4835840 GSM4835845 GSM4835849 GSM4835850 GSM4835854 GSM4835859 GSM4835861 GSM4835862 GSM4835866 GSM4835868 GSM4835869 GSM4835870
GSM4835819 GSM4835820 GSM4835821 GSM4835822 GSM4835823 GSM4835824 GSM4835825 GSM4835826 GSM4835827 GSM4835828 GSM4835830 GSM4835831 GSM4835832 GSM4835835 GSM4835837 GSM4835838 GSM4835841 GSM4835843 GSM4835844 GSM4835846 GSM4835847 GSM4835852 GSM4835853 GSM4835855 GSM4835856 GSM4835858 GSM4835863 GSM4835867
Description

Submission Date: Oct 19, 2020

Summary: We report on the application of RNA sequencing to study expression changes in cardiac surgery patients of different obesity categories.

GEO Accession ID: GSE159612

PMID: 35082386

Description

Submission Date: Oct 19, 2020

Summary: We report on the application of RNA sequencing to study expression changes in cardiac surgery patients of different obesity categories.

GEO Accession ID: GSE159612

PMID: 35082386

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.