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
Pancreatic Progenitor Cells
GSM3559822 GSM3559823
GSM3559820 GSM3559821
GSM3559824 GSM3559825
Description

Submission Date: Jan 10, 2019

Summary: Adult pancreatic progenitor cells are a potential source of renewable insulin producing cells that can be used in regenerative medicine and cell replacement therapy for type-1 diabetes. We sought to understand the mechanisms by which adult pancreatic progenitor cells undergo self-renewal. The effects of knockdown of Glis3, CD133 (Prominin-1) and beta-catenin were investigated and we found that Glis3 and CD133 are capable of regulating self-renewal through Wnt gene expression.

GEO Accession ID: GSE124944

PMID: No Pubmed ID

Description

Submission Date: Jan 10, 2019

Summary: Adult pancreatic progenitor cells are a potential source of renewable insulin producing cells that can be used in regenerative medicine and cell replacement therapy for type-1 diabetes. We sought to understand the mechanisms by which adult pancreatic progenitor cells undergo self-renewal. The effects of knockdown of Glis3, CD133 (Prominin-1) and beta-catenin were investigated and we found that Glis3 and CD133 are capable of regulating self-renewal through Wnt gene expression.

GEO Accession ID: GSE124944

PMID: No Pubmed ID

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.