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
Human pancreatic fat
GSM5208135 GSM5208136 GSM5208137 GSM5208138 GSM5208139 GSM5208140 GSM5208141 GSM5208142 GSM5208143 GSM5208144 GSM5208145 GSM5208146
GSM5208123 GSM5208124 GSM5208125 GSM5208126 GSM5208127 GSM5208128 GSM5208129 GSM5208130 GSM5208131 GSM5208132 GSM5208133 GSM5208134
Description

Submission Date: Mar 24, 2021

Summary: The aim of this study is to investigate the impact of the metabolic status on the transcriptome of isolated preadipocytes and in vitro differentiated adipocytes. We identified 38654 transcripts in pancreatic fat cells. We report that preadipocyte differentiation increased the abundance of mRNA levels of proteins related to adipogenesis and lipid metabolism. These changes in the transcriptome were absent or less pronounced in fat cells obtained from patients with prediabetes and type 2 diabetes. Meanwhile, mRNA levels of proteins involved in excellular matrix remodeling, angiogenesis and cytoskeleton organization were less abundant in adipocytes of all metabolic groups.

GEO Accession ID: GSE169514

PMID: 33788629

Description

Submission Date: Mar 24, 2021

Summary: The aim of this study is to investigate the impact of the metabolic status on the transcriptome of isolated preadipocytes and in vitro differentiated adipocytes. We identified 38654 transcripts in pancreatic fat cells. We report that preadipocyte differentiation increased the abundance of mRNA levels of proteins related to adipogenesis and lipid metabolism. These changes in the transcriptome were absent or less pronounced in fat cells obtained from patients with prediabetes and type 2 diabetes. Meanwhile, mRNA levels of proteins involved in excellular matrix remodeling, angiogenesis and cytoskeleton organization were less abundant in adipocytes of all metabolic groups.

GEO Accession ID: GSE169514

PMID: 33788629

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

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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.