Using Seriation to Find Patterns in Gene Expression Data
Author Information
Author(s): Morozova Olena, Morozov Vyacheslav, Hoffman Brad G., Helgason Cheryl D., Marra Marco A.
Primary Institution: Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, Canada
Hypothesis
Can seriation provide a more accurate method for identifying co-expressed genes in SAGE data compared to clustering methods?
Conclusion
Seriation is able to identify groups of co-expressed genes more accurately than a clustering algorithm developed specifically for SAGE data.
Supporting Evidence
- Seriation performed better than a SAGE-specific clustering method on noisy SAGE data.
- The seriation method produced fewer false positives compared to clustering.
- Seriation revealed temporal relationships among gene expression patterns.
Takeaway
This study shows that a new way of organizing gene data called seriation can help scientists find groups of genes that work together better than older methods.
Methodology
The study used a seriation heuristic called 'progressive construction of contigs' to analyze simulated and experimental SAGE data.
Potential Biases
Potential biases may arise from the selection of data and the inherent limitations of the seriation method.
Limitations
The performance of seriation may vary with different datasets and the method may not capture all relevant gene interactions.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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