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Full Description
This valuable collection aims to provide a collection of frequently used statistical methods in the field of proteomics. Although there is a large overlap between statistical methods for the different 'omics' fields, methods for analyzing data from proteomics experiments need their own specific adaptations. To satisfy that need, Statistical Analysis in Proteomics focuses on the planning of proteomics experiments, the preprocessing and analysis of the data, the integration of proteomics data with other high-throughput data, as well as some special topics. Written for the highly successful Methods in Molecular Biology series, the chapters contain the kind of detail and expert implementation advice that makes for a smooth transition to the laboratory.
Practical and authoritative, Statistical Analysis in Proteomics serves as an ideal reference for statisticians involved in the planning and analysis of proteomics experiments, beginners as well as advanced researchers, and also for biologists, biochemists, and medical researchers who want to learn more about the statistical opportunities in the analysis of proteomics data.
Contents
Introduction to Proteomics Technologies.- Topics in Study Design and Analysis for Multi-Stage Clinical Proteomics Studies.- Preprocessing and Analysis of LC-MS-Based Proteomic Data.- Normalization of Reverse Phase Protein Microarray Data: Choosing the Best Normalization Analyte.- Outlier Detection for Mass Spectrometric Data.- Visualization and Differential Analysis of Protein Expression Data Using R.- False Discovery Rate Estimation in Proteomics.- A Nonparametric Bayesian Model for Nested Clustering.- Set-Based Test Procedures for the Functional Analysis of Protein Lists from Differential Analysis.- Classification of Samples with Order Restricted Discriminant Rules.- Application of Discriminant Analysis and Cross Validation on Proteomics Data.- Protein Sequence Analysis by Proximities.- Statistical Method for Integrative Platform Analysis: Application to Integration of Proteomic and Microarray Data.- Data Fusion in Metabolomics and Proteomics for Biomarkers Discovery.- Reconstruction of Protein Networks Using Reverse Phase Protein Array Data.- Detection of Unknown Amino Acid Substitutions Using Error-Tolerant Database Search.- Data Analysis Strategies for Protein Modification Identification.- Dissecting the iTRAQ DataAnalysis.- Statistical Aspects in Proteomic Biomarker Discovery.
