Analyzing Protein Arrays Using Gene Ontology Clustering
Author Information
Author(s): Cheryl Wolting, C Jane McGlade, David Tritchler
Primary Institution: University of Toronto
Hypothesis
Can clustering proteins by their Gene Ontology annotations reveal new insights into their functions?
Conclusion
The methodology developed allows for better identification of protein functions within high-throughput datasets, leading to new hypotheses.
Supporting Evidence
- The methodology reveals trends and enrichment of proteins of particular functions.
- Clustering can identify novel associations that are not obvious when proteins are analyzed individually.
- 78% of cluster labels selected were statistically enriched GO terms.
Takeaway
This study shows how we can group proteins based on their functions to find new patterns and ideas about how they work together.
Methodology
The study used a graph similarity distance measure and Partitioning Around Medoids (PAM) clustering to analyze protein annotations.
Potential Biases
Potential biases in GO annotations could affect clustering results.
Limitations
The clustering results may not always reveal clear biological structures due to the complexity of GO annotations.
Participant Demographics
The study focused on yeast proteins.
Statistical Information
P-Value
p<0.01
Statistical Significance
p<0.01
Digital Object Identifier (DOI)
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