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
bladder cancer cell line 5637
GSM4579765 GSM4579767 GSM4579769
GSM4579766 GSM4579768 GSM4579770
bladder cancer cell line CLS-439
GSM4579777 GSM4579779 GSM4579781
GSM4579778 GSM4579780 GSM4579782
bladder cancer cell line T24
GSM4579759 GSM4579761 GSM4579763
GSM4579760 GSM4579762 GSM4579764
bladder cancer cell line TCC-SUP
GSM4579771 GSM4579773 GSM4579775
GSM4579772 GSM4579774 GSM4579776
Description

Submission Date: May 30, 2020

Summary: Mitomycin C (MMC) is the gold standard treatment for non-muscle invasive bladder cancer (NMIBC). We aimed to evaluate in bladder cancer cell lines (T24, 5637, TCC-SUP and CLS-439) the transcriptomic response to MMC treatment. We used Gene Set Enrichment Analysis (GSEA) to identify biological processes and pathways modulated by MMC treatment.

GEO Accession ID: GSE151505

PMID: 33408185

Description

Submission Date: May 30, 2020

Summary: Mitomycin C (MMC) is the gold standard treatment for non-muscle invasive bladder cancer (NMIBC). We aimed to evaluate in bladder cancer cell lines (T24, 5637, TCC-SUP and CLS-439) the transcriptomic response to MMC treatment. We used Gene Set Enrichment Analysis (GSEA) to identify biological processes and pathways modulated by MMC treatment.

GEO Accession ID: GSE151505

PMID: 33408185

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.