The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data
2007
Wavelet Analysis for Gene Expression Data
Sample size: 72
publication
Evidence: moderate
Author Information
Author(s): Song JZ, Duan KM, Ware T, Surette M
Primary Institution: University of Maryland, College Park, MD, USA
Hypothesis
Can wavelet analysis improve the comparison of temporal gene expression data obtained under different growth conditions?
Conclusion
Wavelet analysis can effectively transform temporal gene expression data for better comparison across different growth conditions.
Supporting Evidence
- Wavelet analysis allows for the comparison of gene expression patterns even when time shifts occur.
- This method can be applied to analyze data sets involving thousands of genes.
Takeaway
This study shows that using wavelet analysis helps scientists compare how genes behave over time, even when experiments are done under different conditions.
Methodology
The study used wavelet analysis to transform temporal gene expression data for comparison across different growth conditions.
Digital Object Identifier (DOI)
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