TBrowser: A Tool for Analyzing Gene Expression Data
Author Information
Author(s): Lopez Fabrice, Textoris Julien, Bergon Aurélie, Didier Gilles, Remy Elisabeth, Granjeaud Samuel, Imbert Jean, Nguyen Catherine, Puthier Denis
Primary Institution: Inserm U928, TAGC, Parc Scientifique de Luminy, Marseille, France
Hypothesis
Can a new application improve access to and analysis of gene expression data from public microarray repositories?
Conclusion
The TBrowser application effectively identifies transcriptional signatures and gene networks from large datasets, enhancing the analysis of gene expression data.
Supporting Evidence
- TBrowser was used to define breast cancer cell specific genes and detect chromosomal abnormalities in tumors.
- 84% of transcriptional signatures showed over-representation of functional terms.
- DBF-MCL algorithm effectively filtered out uninformative profiles and identified natural clusters in datasets.
Takeaway
TBrowser is like a super-smart search engine for gene data that helps scientists find important information quickly and easily.
Methodology
The study used a modified Markov clustering algorithm to extract clusters of co-regulated genes from 1,484 microarray datasets.
Potential Biases
Potential bias due to the use of normalized data instead of raw data.
Limitations
The quality of individual samples could not be determined due to reliance on normalized data provided by submitters.
Digital Object Identifier (DOI)
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