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
venous blood
GSM5360191 GSM5360192 GSM5360193 GSM5360194
GSM5360211 GSM5360212 GSM5360213 GSM5360214
GSM5360167 GSM5360168 GSM5360169 GSM5360170
GSM5360195 GSM5360196 GSM5360197 GSM5360198
GSM5360183 GSM5360184 GSM5360185 GSM5360186
GSM5360199 GSM5360200 GSM5360201 GSM5360202
GSM5360203 GSM5360204 GSM5360205 GSM5360206
GSM5360187 GSM5360188 GSM5360189 GSM5360190
GSM5360179 GSM5360180 GSM5360181 GSM5360182
GSM5360175 GSM5360176 GSM5360177 GSM5360178
GSM5360207 GSM5360208 GSM5360209 GSM5360210
GSM5360171 GSM5360172 GSM5360173 GSM5360174
Description

Submission Date: Jun 05, 2021

Summary: Purpose: To characterise the transcriptomic landscape in monocytes associated with IRF5 expression

Methods: RNA sequencing from FACS sorted IRF5+ and IRF5- CD14+ monocytes

Results: Differential expression based on IRF5 postiivity provides insight into its roles in monocyte function and in type-2 diabetes

Conclusions: This study represents the first analyses of IRF5-dependent transcriptome in circulating monocytes from patients with Type-2 diabetes

GEO Accession ID: GSE176216

PMID: 36042203

Description

Submission Date: Jun 05, 2021

Summary: Purpose: To characterise the transcriptomic landscape in monocytes associated with IRF5 expression

Methods: RNA sequencing from FACS sorted IRF5+ and IRF5- CD14+ monocytes

Results: Differential expression based on IRF5 postiivity provides insight into its roles in monocyte function and in type-2 diabetes

Conclusions: This study represents the first analyses of IRF5-dependent transcriptome in circulating monocytes from patients with Type-2 diabetes

GEO Accession ID: GSE176216

PMID: 36042203

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

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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.