GSE140627: Urinary sediment transcriptomic and longitudinal data to investigate renal function decline in type 1 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
urine sediment cells
GSM4175969 GSM4175970
GSM4175971 GSM4175972 GSM4175973 GSM4175974 GSM4175975 GSM4175976 GSM4175977 GSM4175978
Description

Submission Date: Nov 18, 2019

Summary: Using a discovery/validation approach we investigated associations between a panel of genes selected from a transcriptomic study and the renal function decline across time in a cohort of type 1 diabetes patients.

GEO Accession ID: GSE140627

PMID: 32425885

Description

Submission Date: Nov 18, 2019

Summary: Using a discovery/validation approach we investigated associations between a panel of genes selected from a transcriptomic study and the renal function decline across time in a cohort of type 1 diabetes patients.

GEO Accession ID: GSE140627

PMID: 32425885

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.