Abstract

Mass spectrometry has been used extensively in recent years as a valuable tool in the study of proteomics. However, the data thus produced exhibits hyper-dimensionality. Reducing the dimensionality of the data often requires the imposition of many assumptions which can be harmful to subsequent analysis. The IP algorithm is a dimension reduction algorithm, similar in purpose to latent variable analysis. It is based on the principle of maximum entropy and therefore imposes a minimum number of assumptions on the data. Partial Least Squares (PLS) is an algorithm commonly used with proteomics data from mass spectrometry in order to reduce the dimension of the data. The IP algorithm and a PLS algorithm were applied to proteomics data from mass spectrometry to reduce the dimension of the data. The data came from three groups of patients, those with no tumors, malignant or benign tumors. Reduced data sets were produced from the IP algorithm and the PLS algorithm. Logistic regression models were constructed using predictor variables extracted from these data sets. The response was threefold and indicated which tumor classifications each patient belonged. Misclassification rates were determined for the IP algorithm and the PLS algorithm. The rates correct classification associated with the IP algorithm were equal or better than those rates associated with the PLS algorithm.

Degree

MS

College and Department

Physical and Mathematical Sciences; Statistics

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2004-11-30

Document Type

Selected Project

Handle

http://hdl.lib.byu.edu/1877/etd618

Keywords

Proteomics, hyper-dimensionality, Partial Least Squares, mass spectrometry, IP algorithm, Information Partition Function, classification, discrimination, ovarian cancer, grade of membership

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