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
Human islet graft
GSM4157059 GSM4157065 GSM4157060 GSM4157061
GSM4157054 GSM4157055 GSM4157056 GSM4157057 GSM4157058
GSM4157062 GSM4157066 GSM4157063 GSM4157064
Description

Submission Date: Nov 12, 2019

Summary: We used RNA-seq to investigate transcriptional changes in human islet grafts that were treated in vivo with tacrolimus or sirolimus. We show that this treatment induces broad transcriptional dysregulation related to peptide processing, ion/calcium flux, and the extracellular matrix.

GEO Accession ID: GSE140230

PMID: 31941840

Description

Submission Date: Nov 12, 2019

Summary: We used RNA-seq to investigate transcriptional changes in human islet grafts that were treated in vivo with tacrolimus or sirolimus. We show that this treatment induces broad transcriptional dysregulation related to peptide processing, ion/calcium flux, and the extracellular matrix.

GEO Accession ID: GSE140230

PMID: 31941840

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.