Statistical Analysis in Proteomics

ISBN-13: 9781493979875 / Angielski / Miękka / 2019 / 313 str.

Klaus Jung

Opis

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.

Spis treści

Part I: Proteomics, Study Design, and Data Processing

 

1. Introduction to Proteomics Technologies

            Christof Lenz and Hassan Dihazi

 

2. Topics in Study Design and Analysis for Multi-Stage Clinical Proteomics Studies

            Irene Sui Lan Zeng

 

3. Preprocessing and Analysis of LC-MS-Based Proteomic Data

            Tsung-Heng Tsai, Minkun Wang, and Habtom W. Ressom

 

4. Normalization of Reverse Phase Protein Microarray Data: Choosing the Best Normalization Analyte

            Antonella Chiechi

 

5. Outlier Detection for Mass Spectrometric Data

            HyungJun Cho and Soo-Heang Eo

 

Part II: Group Comparisons

 

6. Visualization and Differential Analysis of Protein Expression Data Using R

            Tomé S. Silva and Nadège Richard

 

7. False Discovery Rate Estimation in Proteomics

            Suruchi Aggarwal and Amit Kumar Yadav

 

8. A Nonparametric Bayesian Model for Nested Clustering

            Juhee Lee, Peter Müller, Yitan Zhu, and Yuan Ji

 

9. Set-Based Test Procedures for the Functional Analysis of Protein Lists from Differential Analysis

            Jochen Kruppa and Klaus Jung

 

Part III: Classification Methods

 

10. Classification of Samples with Order Restricted Discriminant Rules

            David Conde, Miguel A. Fernández, Bonifacio Salvador, and Cristina Rueda

 

11. Application of Discriminant Analysis and Cross Validation on Proteomics Data

            Julia Kuligowski, David Pérez-Guaita, and Guillermo Quintás

 

12. Protein Sequence Analysis by Proximities

            Frank-Michael Schleif

 

Part IV: Data Integration

 

13. Statistical Method for Integrative Platform Analysis: Application to Integration of Proteomic and Microarray Data

            Xin Gao

 

14. Data Fusion in Metabolomics and Proteomics for Biomarkers Discovery

            Lionel Blanchet and Agnieszka Smolinska

 

Part V: Special Topics

 

15. Reconstruction of Protein Networks Using Reverse Phase Protein Array Data

            Silvia von der Heyde, Johanna Sonntag, Frank Kramer, Christian Bender, Ulrike Korf, and Tim Beißbarth

 

16. Detection of Unknown Amino Acid Substitutions Using Error-Tolerant Database Search

            Sven H. Giese, Franziska Zickmann, and Bernhard Y. Renard

 

17. Data Analysis Strategies for Protein Modification Identification

            Yan Fu

 

18. Dissecting the iTRAQ Data Analysis

            Suruchi Aggarwal and Amit Kumar Yadav

 

19. Statistical Aspects in Proteomic Biomarker Discovery

            Klaus Jung