Chemical genomics is an interdisciplinary field that combines small molecule perturbation with genomics to understand gene function and to study the mode(s) of drug action. Existing methods for correlating chemical genomic profiles are not ideal as they often require one to define the disrupting effects, commonly known as batch effects. These effects are not always known, and they can mask true biological differences.
I present a method, Bucket Evaluations (BE), which surmounts these problems. This method is a non-parametric correlation approach, which is suitable for locating correlations in somewhat perturbed datasets such as chemical genomic profiles. BE can be used on other datasets such as those obtained via gene expression profiling and performs well on both array-based and sequence based readouts. Using BE, along with various correlation methods, on a collection of datasets, showed it to be highly accurate for locating similarity between experiments.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/32921 |
| Date | 04 September 2012 |
| Creators | Shabtai, Daniel |
| Contributors | Nislow, Corey, Westwood, J. Timothy |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
Page generated in 0.0022 seconds