GSE150586: Next generation sequencing identifies differentially expressed genes between breast cancer with diabetes and breast cancer without diabetes

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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GROUP CONDITION SAMPLES
Breast cancer
GSM4552765 GSM4552766 GSM4552767 GSM4552768 GSM4552769 GSM4552770 GSM4552771
GSM4552759 GSM4552760 GSM4552761 GSM4552762 GSM4552763 GSM4552764
Description

Submission Date: May 14, 2020

Summary: We report the application of RNA-sequencing for high-throughput profiling of transcriptomes in tumor tissues from patients with breast cancer and diabetes, and in tumor tissues from breast cancer patients without diabetes.

GEO Accession ID: GSE150586

PMID: No Pubmed ID

Description

Submission Date: May 14, 2020

Summary: We report the application of RNA-sequencing for high-throughput profiling of transcriptomes in tumor tissues from patients with breast cancer and diabetes, and in tumor tissues from breast cancer patients without diabetes.

GEO Accession ID: GSE150586

PMID: No Pubmed ID

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.

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Differential Gene Expression Analysis
Info Differential expression signatures can be computed using DESeq2 or characteristic direction.
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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.