Identifying Differential Correlation in Gene/Pathway Combinations
Author Information
Author(s): Rosemary Braun, Leslie Cope, Giovanni Parmigiani
Primary Institution: National Cancer Institute, National Institutes of Health
Hypothesis
Can known pathway information improve the analysis of microarray data by identifying joint differential expression in gene-pathway pairs?
Conclusion
The method allows for the identification of interactions between genes and pathways that may play a role in disease, particularly in cancer.
Supporting Evidence
- The method identifies gene-pathway pairs with significant differences in correlation across phenotypes.
- Application of the method to cancer datasets yielded promising results.
- High SGPC values indicate potential interactions between genes and pathways.
Takeaway
This study created a new way to look at how genes and pathways work together in cancer, helping to find new connections that could be important for understanding the disease.
Methodology
The study used principal component analysis to summarize gene expression levels for pathways and compared correlations between gene expression and pathway summaries across different phenotypes.
Potential Biases
Potential bias may arise from the selection of pathways and genes that are already known, which could influence the results.
Limitations
The method relies on the quality of the pathway annotations and may miss interactions if the pathways are not well-defined.
Participant Demographics
The study analyzed data from normal and tumor prostate cell samples as well as lung cancer samples.
Statistical Information
P-Value
<1e-04
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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