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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Select conditions below to toggle them from the plot:

GROUP CONDITION SAMPLES
human islet
GSM2909349 GSM2909350 GSM2909351
GSM2909352 GSM2909353
GSM2909330 GSM2909331 GSM2909332 GSM2909333
GSM2909338 GSM2909339
GSM2909334 GSM2909335 GSM2909336 GSM2909337
Description

Submission Date: Jan 02, 2018

Summary: Comparative RNA and small RNA sequencing analysis of in vitro differentiated β like cells, in vivo matured β cells, adult islet cells, and Lin28 deficient in vitro differentiated β like cells

GEO Accession ID: GSE108654

PMID: 31883920

Description

Submission Date: Jan 02, 2018

Summary: Comparative RNA and small RNA sequencing analysis of in vitro differentiated β like cells, in vivo matured β cells, adult islet cells, and Lin28 deficient in vitro differentiated β like cells

GEO Accession ID: GSE108654

PMID: 31883920

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.