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Gene counts are sourced from ARCHS4, which provides uniform alignment of GEO samples.
You can learn more about ARCHS4 and its pipeline here.
Select conditions below to toggle them from the plot:
| GROUP | CONDITION | SAMPLES |
|---|---|---|
| Fibrovascular Membrane |
GSM2467181 GSM2467182 GSM2467183 GSM2467184 GSM2467185 GSM2467186 GSM2467187 GSM2467188 GSM2467189
|
|
| Retina |
GSM2467179 GSM2467180 GSM2467190 GSM2467191
|
Submission Date: Jan 24, 2017
Summary: Purpose: Identification of RUNX1 via next-generation sequencing (NGS) of fibrovascular membranes in patients with proliferative diabetic retinopathy.
Methods: Transcriptomic analysis with Illumina HiSeq2000 of fibrovascular membrane and control retina CD31+ samples. The sequence reads were analyzed with ANOVA (ANOVA) and targets with significance (fold change > +/-1.5 and p-value < 0.05) were selected for with Cufflinks, DeSeq2, Partek E/M, and EdgeR. qRT–PCR validation was performed using SYBR Green assays along with Western blots, siRNA, and MUSE proliferation assays.
Results: Using an optimized data analysis workflow, we mapped sequence reads per sample to the human genome (hg19) and identified genes that were statistically significant in all four statistical packages. P-values ranged from 8.78E-10 to 0.05. Using this gene list for ontology, highly significant annotation clusters included inflammatory, vascular development, and cell adhesion pathways.
Conclusions: Our study represents the first detailed transcriptomic analysis of CD31+ cells from fibrovascular membrane and CD31+ cells from control retinas with biologic replicates, generated by RNA-seq technology. The preferential selection of inflammatory and angiogenic pathways using this gene list is highly consistent with DR pathogenesis, which involves leaky and aberrant vessel growth.
GEO Accession ID: GSE94019
PMID: 28400392
Submission Date: Jan 24, 2017
Summary: Purpose: Identification of RUNX1 via next-generation sequencing (NGS) of fibrovascular membranes in patients with proliferative diabetic retinopathy.
Methods: Transcriptomic analysis with Illumina HiSeq2000 of fibrovascular membrane and control retina CD31+ samples. The sequence reads were analyzed with ANOVA (ANOVA) and targets with significance (fold change > +/-1.5 and p-value < 0.05) were selected for with Cufflinks, DeSeq2, Partek E/M, and EdgeR. qRT–PCR validation was performed using SYBR Green assays along with Western blots, siRNA, and MUSE proliferation assays.
Results: Using an optimized data analysis workflow, we mapped sequence reads per sample to the human genome (hg19) and identified genes that were statistically significant in all four statistical packages. P-values ranged from 8.78E-10 to 0.05. Using this gene list for ontology, highly significant annotation clusters included inflammatory, vascular development, and cell adhesion pathways.
Conclusions: Our study represents the first detailed transcriptomic analysis of CD31+ cells from fibrovascular membrane and CD31+ cells from control retinas with biologic replicates, generated by RNA-seq technology. The preferential selection of inflammatory and angiogenic pathways using this gene list is highly consistent with DR pathogenesis, which involves leaky and aberrant vessel growth.
GEO Accession ID: GSE94019
PMID: 28400392
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:
No precomputed signatures are currently available for this study. You can compute differential gene expression on the fly below:
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.
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.
