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Estimating the Overlap of Top Instances in Lists Ranked by Correlation to Label Open Access
- Other title
Genome wide association studies
- Type of item
- Degree grantor
University of Alberta
- Author or creator
- Supervisor and department
Russell Greiner (Computing Science)
- Examining committee member and department
Csaba Szepesvari (Computing Science)
Sambasivarao Damaraju (Cross Cancer Institute)
Department of Computing Science
- Date accepted
- Graduation date
Master of Science
- Degree level
Recent advances in high-throughput technologies, such as genome-wide SNP analysis and microar- ray gene expression profiling, have led to a multitude of ranked lists, where the features (SNPs, genes) are sorted based on their individual correlation with a phenotype. Multiple reviews have shown that most such rankings vary considerably across different studies, even in the case of sub- sampling from a single dataset. This motivates our interest in formally investigating the overlap of the top ranked features in two lists sorted by correlation with an outcome.
This dissertation presents a mathematical model for better understanding lists whose entries are ranked by Pearson correlation coefficient with an outcome. We show that our model is able to accurately predict the expected overlap between two ranked lists based on reasonable assumptions. We also discuss how to generalize this model to find the overlap between other forms of rankings, provided that they satisfy mild assumptions.
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