Statistical Analysis of Proteomic Data (Hardcover). Cet article n’est pas disponible.
Langue : anglais
Edité par Springer-Verlag New York Inc., New York, NY, 2022
- Livre relié
- Neuf
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Vendeur AbeBooks depuis 22 juin 2007
Etat: Neuf
EUR 351,71
A propos de cet article
Hardcover. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…
N° de réf. du vendeur 9781071619667
- Titre
- Statistical Analysis of Proteomic Data (Hardcover)
- Auteur
- Thomas Burger
- Éditeur
- Springer-Verlag New York Inc., New York, NY
- Année de publication
- 2022
- État de l'article
- new
- Reliure
- Hardcover
- Langue
- anglais
- ISBN à 10 chiffres
- 1071619667
- ISBN à 13 chiffres
- 9781071619667
- Série
- Livre 186 sur 382: Methods in Molecular Biology
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. Inte
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