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
liver hepatocellular carcinoma
GSM4685036 GSM4685037 GSM4685038 GSM4685039 GSM4685040
GSM4685031 GSM4685032 GSM4685033 GSM4685034 GSM4685035
Description

Submission Date: Jul 23, 2020

Summary: To validate CXCL12 and IL7R expression between cancerous and paracancerous, five cancer and five paracancerous tissues,wihch were obtained from five LIHC patients undergoing liver cancer surgery at the Guangdong Second Provincial General Hospital,were performed RNA-seq. The RNA-seq data showed a significant difference in CXCL12 expression between cancerous and paracancerous tissues (p =0.04), as well as the IL7R expression (p<0.01). There was also a significant difference between cancerous and paracancerous samples in NK cells (resting) (p = 0.0014), M1 macrophages (p = 0.0058), CD8+ T cells (p = 0.0026), and Tregs (p = 0.0015) in the immunocyte infiltration analysis.

GEO Accession ID: GSE154964

PMID: 33344233

Description

Submission Date: Jul 23, 2020

Summary: To validate CXCL12 and IL7R expression between cancerous and paracancerous, five cancer and five paracancerous tissues,wihch were obtained from five LIHC patients undergoing liver cancer surgery at the Guangdong Second Provincial General Hospital,were performed RNA-seq. The RNA-seq data showed a significant difference in CXCL12 expression between cancerous and paracancerous tissues (p =0.04), as well as the IL7R expression (p<0.01). There was also a significant difference between cancerous and paracancerous samples in NK cells (resting) (p = 0.0014), M1 macrophages (p = 0.0058), CD8+ T cells (p = 0.0026), and Tregs (p = 0.0015) in the immunocyte infiltration analysis.

GEO Accession ID: GSE154964

PMID: 33344233

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.