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
Whole cardiac tissue
GSM2835520 GSM2835521 GSM2835522
GSM2835517 GSM2835518 GSM2835519
GSM2835515 GSM2835516
GSM2835512 GSM2835513 GSM2835514
Description

Submission Date: Oct 27, 2017

Summary: Our study aims to analyze the effect in whole cardiac transcriptome of cardiac macrophage depletion and the lack of receptors implicated in phagocytosis.

GEO Accession ID: GSE106295

PMID: No Pubmed ID

Description

Submission Date: Oct 27, 2017

Summary: Our study aims to analyze the effect in whole cardiac transcriptome of cardiac macrophage depletion and the lack of receptors implicated in phagocytosis.

GEO Accession ID: GSE106295

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.
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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.