Files in this item



application/pdf2_Yang_Ji-Yeon.pdf (1MB)
(no description provided)PDF


Title:Statistical modeling of protein lysate array data
Author(s):Yang, Ji Yeon
Director of Research:He, Xuming
Doctoral Committee Chair(s):He, Xuming
Doctoral Committee Member(s):Douglas, Jeffrey A.; Liang, Feng; Qu, Annie
Department / Program:Statistics
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Confidence intervals
Dilution series
Maximum likelihood estimator (MLE)
Non-i.i.d. Model
Nonlinear mixed effects model
Nonlinear optimization
Protein lysate array quantification
Sigmoidal model
Abstract:The protein lysate array is an emerging technology for quantifying the protein concentration ratios in multiple biological samples. It is gaining popularity, and has the potential to answer questions about post-translational modifications and protein pathway relationships. Statistical inference for a parametric quantification procedure has been inadequately addressed in the literature, mainly due to two challenges: the increasing dimension of the parameter space and the need to account for dependence in the data. Each chapter of this thesis addresses one of these issues. In Chapter 1, an introduction to the protein lysate array quantification is presented, followed by the motivations and goals for this thesis work. In Chapter 2, we develop a multi-step procedure for the Sigmoidal models, ensuring consistent estimation of the concentration level with full asymptotic efficiency. The results obtained in this chapter justify inferential procedures based on large-sample approximations. Simulation studies and real data analysis are used to illustrate the performance of the proposed method in finite-samples. The multi-step procedure is simpler in both theory and computation than the single-step least squares method that has been used in current practice. In Chapter 3, we introduce a new model to account for the dependence structure of the errors by a nonlinear mixed effects model. We consider a method to approximate the maximum likelihood estimator of all the parameters. Using the simulation studies on various error structures, we show that for data with non-i.i.d. errors the proposed method leads to more accurate estimates and better confidence intervals than the existing single-step least squares method.
Issue Date:2010-08-31
Rights Information:Copyright 2010 Ji Yeon Yang
Date Available in IDEALS:2010-08-31
Date Deposited:2010-08

This item appears in the following Collection(s)

Item Statistics