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
Skin
GSM4288333 GSM4288335 GSM4288336 GSM4288337 GSM4288338 GSM4288339 GSM4288340 GSM4288341 GSM4288342 GSM4288343 GSM4288344
GSM4288318 GSM4288319 GSM4288320 GSM4288321 GSM4288322 GSM4288323 GSM4288324 GSM4288325 GSM4288327 GSM4288328 GSM4288329 GSM4288330 GSM4288332
Description

Submission Date: Jan 29, 2020

Summary: We investigated genome wide changes in gene expression in skin between patients with type 2 diabetes and non-diabetic patients using next generation sequencing. We compared the gene expression in skin samples taken from 27 patients (13 with type 2 diabetes and 14 non-diabetic).

GEO Accession ID: GSE144441

PMID: No Pubmed ID

Description

Submission Date: Jan 29, 2020

Summary: We investigated genome wide changes in gene expression in skin between patients with type 2 diabetes and non-diabetic patients using next generation sequencing. We compared the gene expression in skin samples taken from 27 patients (13 with type 2 diabetes and 14 non-diabetic).

GEO Accession ID: GSE144441

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.

Signatures:

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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.