Novel implementation of conditional co-regulation by graph theory to derive co-expressed genes from microarray data
2008
New Method to Find Co-Expressed Genes Using Graph Theory
Sample size: 493
publication
Evidence: moderate
Author Information
Author(s): Rawat Arun, Deng Youping
Primary Institution: University of Southern Mississippi
Hypothesis
Can a new graph theory-based method improve the identification of co-expressed genes from microarray data?
Conclusion
The new method allows for the identification of novel relationships among genes by including all genes regardless of their expression values.
Supporting Evidence
- The new method includes all genes, even those with low expression, providing a more complete analysis.
- The algorithm generates a score based on the similarity of expression patterns among genes.
- The study found that many genes identified by the new method also had high confidence in existing databases.
Takeaway
This study created a new way to find genes that work together by looking at their expression patterns, even if some genes aren't always active.
Methodology
The study used a graph theory-based algorithm to analyze microarray data and identify relationships among genes.
Limitations
The method may still miss some relationships that are not captured by the algorithm.
Statistical Information
P-Value
3.15e-08
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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