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
Esophagus Fibroblast
GSM4260266 GSM4260269 GSM4260272 GSM4260275
GSM4260267 GSM4260270 GSM4260273 GSM4260276
GSM4260265 GSM4260268 GSM4260271 GSM4260274
Description

Submission Date: Jan 12, 2020

Summary: To understand the transcriptional responses induced by LIGHT in esophageal fibroblasts and compare them to the pro-fibrotic TGF-B1

GEO Accession ID: GSE143482

PMID: 32712105

Description

Submission Date: Jan 12, 2020

Summary: To understand the transcriptional responses induced by LIGHT in esophageal fibroblasts and compare them to the pro-fibrotic TGF-B1

GEO Accession ID: GSE143482

PMID: 32712105

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.