GSE121632: Gene expression analysis of a panel of human fibroblasts that generate an aligned (anisotropic) vs non-aligned extracellular matrix

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
fibroblast
GSM3440295 GSM3440296 GSM3440297
GSM3440307 GSM3440308
GSM3440309 GSM3440310
GSM3440304 GSM3440305 GSM3440306
GSM3440298 GSM3440299 GSM3440300
GSM3440301 GSM3440302 GSM3440303
Description

Submission Date: Oct 22, 2018

Summary: A panel of human fibroblasts derived from patient samples (vulval, oral, cervical and breast carcinoma and metastatic melanoma) were seperated according to their ability to generate an aligned (anisotropic) or non-aligned extracellular matrix.

GEO Accession ID: GSE121632

PMID: 31659294

Description

Submission Date: Oct 22, 2018

Summary: A panel of human fibroblasts derived from patient samples (vulval, oral, cervical and breast carcinoma and metastatic melanoma) were seperated according to their ability to generate an aligned (anisotropic) or non-aligned extracellular matrix.

GEO Accession ID: GSE121632

PMID: 31659294

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.