Methods of Microarray Data Analysis

✍ Scribed by Jennifer S. Shoemaker, Simon M. Lin (eds.)

• Publisher: Springer US
• Year: 2005
• Tongue: English
• Leaves: 266
• Edition: 1
• Category: Library

✦ Synopsis

As studies using microarray technology have evolved, so have the data analysis methods used to analyze these experiments. The CAMDA conference plays a role in this evolving field by providing a forum in which investors can analyze the same data sets using different methods. METHODS OF MICROARRAY DATA ANALYSIS IV is the fourth book in this series, and focuses on the important issue of associating array data with a survival endpoint. Previous books in this series focused on classification (Volume I), pattern recognition (Volume II), and quality control issues (Volume III).

In this volume, four lung cancer data sets are the focus of analysis. We highlight three tutorial papers, including one to assist with a basic understanding of lung cancer, a review of survival analysis in the gene expression literature, and a paper on replication. In addition, 14 papers presented at the conference are included. This book is an excellent reference for academic and industrial researchers who want to keep abreast of the state-of-the-art of microarray data analysis.

Jennifer Shoemaker is a faculty member in the Department of Biostatistics and Bioinformatics and the Director of the Bioinformatics Unit for the Cancer and Leukemia Group B Statistical Center, Duke University Medical Center. Simon Lin is a faculty member in the Department of Biostatistics and Bioinformatics and the Manager of the Duke Bioinformatics Shared Resource, Duke University Medical Center.

✦ Table of Contents

Introduction....Pages 1-8
Cancer: Clinical Challenges and Opportunities....Pages 9-20
Gene Expression Data and Survival Analysis....Pages 21-34
The Needed Replicates of Arrays in Microarray Experiments for Reliable Statistical Evaluation....Pages 35-49
Pooling Information Across Different Studies and Oligonucleotide Chip Types to Identify Prognostic Genes for Lung Cancer....Pages 51-66
Application of Survival and Meta-analysis to Gene Expression Data Combined from Two Studies....Pages 67-80
Making Sense of Human Lung Carcinomas Gene Expression Data: Integration and Analysis of Two Affymetrix Platform Experiments....Pages 81-94
Entropy and Survival-based Weights to Combine Affymetrix Array Types and Analyze Differential Expression and Survival....Pages 95-108
Associating Microarray Data with a Survival Endpoint....Pages 109-120
Differential Correlation Detects Complex Associations Between Gene Expression and Clinical Outcomes in Lung Adenocarcinomas....Pages 121-131
Probabilistic Lung Cancer Models Conditioned on Gene Expression Microarray Data....Pages 133-146
Integration of Microarray Data for a Comparative Study of Classifiers and Identification of Marker Genes....Pages 147-162
Use of Micro Array Data via Model-based Classification in the Study and Prediction of Survival from Lung Cancer....Pages 163-173
Microarray Data Analysis of Survival Times of Patients with Lung Adenocarcinomas Using ADC and K-Medians Clustering....Pages 175-190
Higher Dimensional Approach for Classification of Lung Cancer Microarray Data....Pages 191-205
Microarray Data Analysis Using Neural Network Classifiers and Gene Selection Methods....Pages 207-222
A Combinatorial Approach to the Analysis of Differential Gene Expression Data....Pages 223-238
Genes Associated with Prognosis in Adenocarcinoma Across Studies at Multiple Institutions....Pages 239-253

✦ Subjects

Human Genetics;Cancer Research