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
Pancreatic islets
GSM2310792 GSM2310793 GSM2310794
GSM2310795 GSM2310796 GSM2310797
Description

Submission Date: Sep 14, 2016

Summary: The goal of this study is to compare the transcriptome of mouse beta-cells expressing mutant constitutively active Glucokinase versus wild-type Glucokinase.

GEO Accession ID: GSE86949

PMID: 27882918

Description

Submission Date: Sep 14, 2016

Summary: The goal of this study is to compare the transcriptome of mouse beta-cells expressing mutant constitutively active Glucokinase versus wild-type Glucokinase.

GEO Accession ID: GSE86949

PMID: 27882918

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.