Principal component tests: applied to temporal gene expression data
2009
Principal Component Tests for Gene Clustering
Sample size: 483
publication
Evidence: moderate
Author Information
Author(s): Zhang Wensheng, Fang Hong-Bin, Song Jiuzhou
Primary Institution: University of Maryland
Hypothesis
Can principal component tests improve the statistical evaluation of clustering methods for gene expression data?
Conclusion
The results demonstrated that the PC testing were useful for determining the optimal number of clusters.
Supporting Evidence
- The PC tests showed that contrasts between clusters were statistically significant.
- The method improved the evaluation of clustering algorithms.
- The study used a public dataset for analysis.
Takeaway
This study shows a new way to check if groups of genes are really different from each other by using special math tests.
Methodology
The study used principal component tests based on exact F statistics to evaluate clustering methods on gene expression data.
Limitations
The study's findings may not apply to all types of datasets or clustering methods.
Statistical Information
P-Value
p<0.01
Statistical Significance
p<0.01
Digital Object Identifier (DOI)
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