Advanced Analysis and Visualization of Gene Copy Number and Expression Data
Author Information
Author(s): Reija Autio, Matti Saarela, Anna-Kaarina Järvinen, Sampsa Hautaniemi, Jaakko Astola
Primary Institution: Tampere University of Technology
Hypothesis
How can gene copy number and expression data be effectively analyzed and visualized in cancer research?
Conclusion
CGH-Plotter v2 and the ECN-tool allow for effective analysis and visualization of gene copy number and expression data, making it easier to identify significant gene alterations.
Supporting Evidence
- CGH-Plotter v2 improves the performance of CGH data analysis.
- The ECN-tool allows for straightforward illustration of copy numbers based on gene expression levels.
- The study identified several known and novel copy number alterations in cancer samples.
Takeaway
This study created tools to help scientists see how many copies of genes are present and how active those genes are, which is important for understanding cancer.
Methodology
The study developed CGH-Plotter v2 and ECN-tool for analyzing and visualizing gene copy number and expression data using MATLAB.
Limitations
The tools may not be applicable for all types of microarrays due to differences in gene sets.
Participant Demographics
The study analyzed data from 38 samples, including 20 head and neck cancer samples and 18 oral tongue cancer samples.
Digital Object Identifier (DOI)
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