Overview

This book explores the most important processing steps of proteomics data analysis and presents practical guidelines, as well as software tools, that are both user-friendly and state-of-the-art in chemo- and biostatistics. Beginning with methods to control the false discovery rate (FDR), the volume continues with chapters devoted to software suites for constructing quantitation data tables, missing value related issues, differential analysis software, and more. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and implementation advice that leads to successful results.  Authoritative and practical, Statistical Analysis of Proteomic Data: Methods and Tools serves as an ideal guide for proteomics researchers looking to extract the best of their data with state-of-the art tools while also deepening their understanding of data analysis.

Full Product Details

Author:   Thomas Burger
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   1st ed. 2023
Volume:   2426
Weight:   0.961kg
ISBN:  

9781071619667

ISBN 10:   1071619667
Pages:   393
Publication Date:   30 October 2022
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

1. Unveiling the Links between Peptide Identification and Differential Analysis FDR Controls by Means of a Practical Introduction to Knockoff Filters             Lucas Etourneau, Nelle Varoquaux, and Thomas Burger   2. A Pipeline for Peptide Detection Using Multiple Decoys             Syamand Hasam, Kristen Emery, William Stafford Noble, and Uri Keich   3. Enhanced Proteomic Data Analysis with MetaMorpheus             Rachel M. Miller, Robert J. Millikin, Zach Rolfs, Michael R. Shortreed, and Lloyd M. Smith   4. Validation of MS/MS Identifications and Label-Free Quantification Using Proline             Véronique Dupierris, Anne-Marie Hesse, Jean-Philippe Menetrey, David Bouyssié, Thomas Burger, Yohann Couté, and Christophe Bruley   5. Integrating Identification and Quantification Uncertainty for Differential Protein Abundance Analysis with Triqler             Matthew The and Lukas Käll   6. Left-Censored Missing Value Imputation Approach for MS-Based Proteomics Data with Gsimp             Runmin Wei and Jingye Wang   7. Towards a More Accurate Differential Analysis of Multiple Imputed Proteomics Data with mi4limma             Marie Chion, Christine Carapito, and Frédéric Bertrand   8. Uncertainty Aware Protein-Level Quantification and Differential Expression Analysis of Proteomics Data with seaMass             Alexander M. Phillips, Richard D. Unwin, Simon J. Hubbard, and Andrew W. Dowsey   9. Statistical Analysis of Quantitative Peptidomics and Peptide-Level Proteomics Data with Prostar             Marianne Tardif, Enora Fremy, Anne-Marie Hesse, Thomas Burger, Yohann Couté, and Samuel Wieczorek   10. msmsEDA and msmsTests: Label-Free Differential Expression by Spectral Counts             Josep Gregori, Àlex Sánchez, and Josep Villanueva   11. Exploring Protein Interactome Data with IPinquiry: Statistical Analysis and Data Visualization by Spectral Counts             Lauriane Kuhn, Timothée Vincent, Philippe Hammann, and Hélène Zuber   12. Statistical Analysis of Post-Translational Modifications Quantified by Label-Free Proteomics Across Multiple Biological Conditions with R: Illustration from SARS-CoV-2 Infected Cells             Quentin Giai Gianetto   13. Fast, Free, and Flexible Peptide and Protein Quantification with FlashLFQ             Robert J. Millikin, Michael R. Shortreed, Mark Scalf, and Lloyd M. Smith   14. Robust Prediction and Protein Selection with Adaptive PENSE             David Kepplinger and Gabriela V. Cohen Freue   15. Multivariate Analysis with the R Package mixOmics             Zoe Welham, Sébastien Déjean, and Kim-Anh Lê Cao   16. Integrating Multiple Quantitative Proteomic Analyses Using MetaMSD             So Young Ryu, Miriam P. Yun, and Sujung Kim   17. Application of WGCNA and PloGO2 in the Analysis of Complex Proteomic Data             Jemma X. Wu, Dana Pascovici, Yunqi Wu, Adam K. Walker, and Mehdi Mirzaei

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