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Published on: July 31, 2017
Exploratory Visual Analysis of statistical results from microarray experiments comparing high and low grade glioma
David M Reif1, Mark A Israel, Jason H Moore
1Computational Genetics Laboratory, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756, USA.
Cancer Informatics
|April 25, 2009
Summary
Exploratory Visual Analysis (EVA) software aids in interpreting gene expression microarray data for brain tumors like glioma. This tool simplifies complex analyses, revealing significant tumor-specific gene patterns and potential therapeutic targets.
Area of Science:
- Genomics
- Bioinformatics
- Neuro-oncology
Background:
- Interpreting gene expression microarray data is challenging, especially for complex diseases like cancer.
- Incorporating expert biological knowledge is crucial for analyzing extensive research data.
Purpose of the Study:
- To present Exploratory Visual Analysis (EVA) software for interpreting gene expression microarray data in glioma.
- To demonstrate EVA's utility in identifying biologically significant gene expression patterns in brain tumors.
Main Methods:
- Utilized Exploratory Visual Analysis (EVA) software, combining statistical analysis with biological annotation.
- Analyzed publicly available gene expression profiles of glioblastoma multiforme (GBM) and pilocytic astrocytoma.
Main Results:
- Identified statistically and biologically significant, tumor class-specific gene expression patterns.
- Highlighted genes involved in cell cycle, proliferation, signaling, adhesion, migration, and structure.
- Identified candidate gene loci on Chromosome 7 implicated in glioma.
Conclusions:
- EVA offers a flexible, visual interface for microarray data interpretation, requiring no specialized statistical or computational expertise.
- EVA facilitates the discovery of biologically relevant gene expression patterns in brain tumors.
- EVA is a valuable tool for computational methods in microarray data analysis.
