GSE124774: RNA-seq of diabetic or non-diabetic murine bone marrow derived macrophages (BMDM)

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.
Enter gene symbol:

Select conditions below to toggle them from the plot:

GROUP CONDITION SAMPLES
Diabetic mice
GSM3553699 GSM3553700 GSM3553703 GSM3553704 GSM3553707 GSM3553708
GSM3553687 GSM3553688 GSM3553691 GSM3553692 GSM3553695 GSM3553696
Wildtype Healthy Control
GSM3553701 GSM3553702 GSM3553705 GSM3553706 GSM3553709 GSM3553710
GSM3553689 GSM3553690 GSM3553693 GSM3553694 GSM3553697 GSM3553698
Description

Submission Date: Jan 07, 2019

Summary: The aim of this study was to interrogate whether BMDM basal or pro-inflammatory stimulated gene expression is altered by diabetes and persists despite in vitro glucose normalisation. Unstimulated BMDM from diabetic mice differentially express 632 genes (324 increased and 308 downregulated) compared to unstimulated control BMDM; this increases to 1,348 genes (802 increased and 546 decreased) upon stimulation (FDR<0.05, FC>1.5).

GEO Accession ID: GSE124774

PMID: No Pubmed ID

Description

Submission Date: Jan 07, 2019

Summary: The aim of this study was to interrogate whether BMDM basal or pro-inflammatory stimulated gene expression is altered by diabetes and persists despite in vitro glucose normalisation. Unstimulated BMDM from diabetic mice differentially express 632 genes (324 increased and 308 downregulated) compared to unstimulated control BMDM; this increases to 1,348 genes (802 increased and 546 decreased) upon stimulation (FDR<0.05, FC>1.5).

GEO Accession ID: GSE124774

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:

Select conditions:

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.