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Raw gene Expression data is sourced from GEO, and the appropriate db package for mapping probes to gene symbols was sourced from the Bioconductor AnnotationData packages.
You can read more about microarray data here.
Select conditions below to toggle them from the plot:
| GROUP | CONDITION | SAMPLES |
|---|---|---|
| Pancreatic Islets |
GSM1941673 GSM1941675 GSM1941680 GSM1941681 GSM1941682 GSM1941684 GSM1941686 GSM1941689 GSM1941690 GSM1941691 GSM1941692 GSM1941693 GSM1941694 GSM1941695 GSM1941696 GSM1941697 GSM1941698 GSM1941699 GSM1941701 GSM1941702 GSM1941703 GSM1941705 GSM1941710 GSM1941711 GSM1941714 GSM1941715 GSM1941716 GSM1941720 GSM1941722 GSM1941724 GSM1941725 GSM1941726 GSM1941729 GSM1941730 GSM1941731
|
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GSM1941674 GSM1941676 GSM1941677 GSM1941678 GSM1941679 GSM1941683 GSM1941685 GSM1941687 GSM1941688 GSM1941700 GSM1941704 GSM1941706 GSM1941707 GSM1941708 GSM1941709 GSM1941712 GSM1941713 GSM1941717 GSM1941718 GSM1941719 GSM1941721 GSM1941723 GSM1941727 GSM1941728
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Submission Date: Nov 16, 2015
Summary: There is growing evidence that transplantation of cadaveric human islets is an effective therapy for type 1 diabetes. However, gauging the suitability of islet samples for clinical use remains a challenge. We hypothesized that islet quality is reflected in the expression of specific genes. Therefore, gene expression in 59 human islet preparations was analyzed and correlated with diabetes reversal after transplantation in diabetic mice. Analysis yielded 262 differentially expressed probesets, which together predict islet quality with 83% accuracy. Pathway analysis revealed that failing islet preparations activated inflammatory pathways, while functional islets showed increased regeneration pathway gene expression. Gene expression associated with apoptosis and oxygen consumption showed little overlap with each other or with the 262 probeset classifier, indicating that the three tests are measuring different aspects of islet cell biology. A subset of 36 probesets surpassed the predictive accuracy of the entire set for reversal of diabetes, and was further reduced by logistic regression to sets of 14 and 5 without losing accuracy. These genes were further validated with an independent cohort of 16 samples. We believe this limited number of gene classifiers in combination with other tests may provide complementary verification of islet quality prior to their clinical use.
GEO Accession ID: GSE75062
PMID: 28968432
Submission Date: Nov 16, 2015
Summary: There is growing evidence that transplantation of cadaveric human islets is an effective therapy for type 1 diabetes. However, gauging the suitability of islet samples for clinical use remains a challenge. We hypothesized that islet quality is reflected in the expression of specific genes. Therefore, gene expression in 59 human islet preparations was analyzed and correlated with diabetes reversal after transplantation in diabetic mice. Analysis yielded 262 differentially expressed probesets, which together predict islet quality with 83% accuracy. Pathway analysis revealed that failing islet preparations activated inflammatory pathways, while functional islets showed increased regeneration pathway gene expression. Gene expression associated with apoptosis and oxygen consumption showed little overlap with each other or with the 262 probeset classifier, indicating that the three tests are measuring different aspects of islet cell biology. A subset of 36 probesets surpassed the predictive accuracy of the entire set for reversal of diabetes, and was further reduced by logistic regression to sets of 14 and 5 without losing accuracy. These genes were further validated with an independent cohort of 16 samples. We believe this limited number of gene classifiers in combination with other tests may provide complementary verification of islet quality prior to their clinical use.
GEO Accession ID: GSE75062
PMID: 28968432
Visualize Samples
Visualizations are precomputed using the Python package scanpy on the top 5000 most variable genes.
Precomputed Differential Gene Expression
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 expression signatures can be computed using DESeq2 or characteristic direction.