Statistical Analysis of Proteomic Data (inbunden)
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Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
393
Utgivningsdatum
2022-10-30
Upplaga
1st ed. 2023
Förlag
Springer-Verlag New York Inc.
Medarbetare
Burger, Thomas (ed.)
Illustrationer
477 Illustrations, color; 50 Illustrations, black and white; XI, 393 p. 527 illus., 477 illus. in co
Dimensioner
254 x 178 x 24 mm
Vikt
922 g
Antal komponenter
1
Komponenter
1 Hardback
ISBN
9781071619667
Statistical Analysis of Proteomic Data (inbunden)

Statistical Analysis of Proteomic Data

Methods and Tools

Inbunden Engelska, 2022-10-30
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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.
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Innehållsförteckning

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 Veronique Dupierris, Anne-Marie Hesse, Jean-Philippe Menetrey, David Bouyssie, Thomas Burger, Yohann Coute, and Christophe Bruley 5. Integrating Identification and Quantification Uncertainty for Differential Protein Abundance Analysis with Triqler Matthew The and Lukas Kall 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 Frederic 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 Coute, and Samuel Wieczorek 10. msmsEDA and msmsTests: Label-Free Differential Expression by Spectral Counts Josep Gregori, Alex Sanchez, and Josep Villanueva 11. Exploring Protein Interactome Data with IPinquiry: Statistical Analysis and Data Visualization by Spectral Counts Lauriane Kuhn, Timothee Vincent, Philippe Hammann, and Helene 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, Sebastien Dejean, and Kim-Anh Le 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