Statistical Analysis of Proteomic Data

ISBN-13: 9781071619698 / Angielski / Miękka / 2023

Opis

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.

Spis treści

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