Li / Xu High-Dimensional Data Analysis in Cancer Research

1. Auflage 2008
ISBN: 978-0-387-69765-9
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 392 Seiten

Reihe: Applied Bioinformatics and Biostatistics in Cancer Research

Multivariate analysis is a mainstay of statistical tools in the analysis of biomedical data. It concerns with associating data matrices of n rows by p columns, with rows representing samples (or patients) and columns attributes of samples, to some response variables, e.g., patients outcome. Classically, the sample size n is much larger than p, the number of variables. The properties of statistical models have been mostly discussed under the assumption of fixed p and infinite n. The advance of biological sciences and technologies has revolutionized the process of investigations of cancer. The biomedical data collection has become more automatic and more extensive. We are in the era of p as a large fraction of n, and even much larger than n. Take proteomics as an example. Although proteomic techniques have been researched and developed for many decades to identify proteins or peptides uniquely associated with a given disease state, until recently this has been mostly a laborious process, carried out one protein at a time. The advent of high throughput proteome-wide technologies such as liquid chromatography-tandem mass spectroscopy make it possible to generate proteomic signatures that facilitate rapid development of new strategies for proteomics-based detection of disease. This poses new challenges and calls for scalable solutions to the analysis of such high dimensional data. In this volume, we will present the systematic and analytical approaches and strategies from both biostatistics and bioinformatics to the analysis of correlated and high-dimensional data.

Autoren/Hrsg.

Li, Xiaochun

Xu, Ronghui

Inhaltsverzeichnis

1;Preface;7
2;Contents;9
3;Contributors;13
4;On the Role and Potential of High-Dimensional Biologic Data in Cancer Research;15
4.1;1.1 Introduction;15
4.2;1.2 Potential of High-Dimensional Data in Biomedical Research;15
4.3;1.3 Statistical Challenges and Opportunities with High- Dimensional Data;20
4.4;1.4 Needed Future Research;23
4.5;References;24
5;Variable Selection in Regression – Estimation, Prediction, Sparsity, Inference;26
5.1;2.1 Overview of Model Selection Methods;26
5.2;2.2 Multivariable Modeling: Penalties/Shrinkage;29
5.3;2.3 Least Angle Regression;34
5.4;2.4 Dantzig Selector;35
5.5;2.5 Prediction and Persistence;38
5.6;2.6 Difficulties with Post-Model Selection Inference;39
5.7;2.7 Penalized Likelihood for Generalized Linear Models;41
5.8;2.8 Simulation Study;41
5.9;2.9 Application of the Methods to the Prostate Cancer Data Set;43
5.10;2.10 Conclusion;45
5.11;References;45
6;Multivariate Nonparametric Regression;47
6.1;3.1 An Example;48
6.2;3.2 Linear and Additive Models;48
6.3;3.3 Interactions;49
6.4;3.4 Basis Function Expansions;51
6.5;3.5 Regression Tree Models;52
6.6;3.6 Spline Models;56
6.7;3.7 Logic Regression;59
6.8;3.8 High-Dimensional Data;62
6.9;3.9 Survival Data;65
6.10;3.10 Discussion;68
6.11;References;68
7;Risk Estimation;71
7.1;4.1 Risk;71
7.2;4.2 Covariance Penalty;72
7.3;4.3 Resampling Methods;77
7.4;4.4 Applications of Risk Estimation;82
7.5;References;91
8;Tree-Based Methods;94
8.1;5.1 Chapter Outline;94
8.2;5.2 Background;95
8.3;5.3 Classification and Regression Trees;96
8.4;5.4 Tree-Based Ensembles;99
8.5;5.5 Example: Prostate Cancer Microarrays;107
8.6;5.6 Software;109
8.7;5.7 Recent Research and Oncology Applications;109
8.8;References;111
9;Support Vector Machine Classification for High- Dimensional Microarray Data Analysis, With Applications in Cancer Research;113
9.1;6.1 Classification Problems: A Statistical Point of View;114
9.2;6.2 Support Vector Machine for Two-Class Classification;117
9.3;6.3 Support Vector Machines for Multiclass Problems;122
9.4;6.4 Parameter Tuning and Solution Path for SVM;124
9.5;6.5 Sparse Learning with Support Vector Machines;125
9.6;6.6 Cancer Data Analysis Using SVM;130
9.7;References;133
10;Bayesian Approaches: Nonparametric Bayesian Analysis of Gene Expression Data;137
10.1;7.1 Introduction;137
10.2;7.2 Bayesian Analysis of Microarray Data;139
10.3;7.3 Nonparametric Bayesian Mixture Model;143
10.4;7.4 Posterior Inference of the Bayesian Model;145
10.5;7.5 Leukemia Gene Expression Example;148
10.6;7.6 Discussion;152
10.7;References;154
11;Index;157