Statistical modeling of protein lysate array data

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Statistics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2010

Author dc:creator

• Yang, Ji Yeon

Contributors dc:contributor

• He, Xuming
• Douglas, Jeffrey A.
• Liang, Feng
• Qu, Annie

• Confidence intervals
• Consistency
• Dilution series
• Maximum likelihood estimator (MLE)
• Non-i.i.d. Model
• Nonlinear mixed effects model
• Nonlinear optimization
• Protein lysate array quantification
• Sigmoidal model

Statement dc:rights

• Copyright 2010 Ji Yeon Yang

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/17036

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/17036