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{ "item_title" : "Statistical Analysis of Proteomic Data", "item_author" : [" Thomas Burger "], "item_description" : "1. Unveiling the Links between Peptide Identification and Differential Analysis FDR Controls by Means of a Practical Introduction to Knockoff FiltersLucas Etourneau, Nelle Varoquaux, and Thomas Burger2. A Pipeline for Peptide Detection Using Multiple DecoysSyamand Hasam, Kristen Emery, William Stafford Noble, and Uri Keich3. Enhanced Proteomic Data Analysis with MetaMorpheusRachel M. Miller, Robert J. Millikin, Zach Rolfs, Michael R. Shortreed, and LloydM. Smith4. Validation of MS/MS Identifications and Label-Free Quantification Using ProlineV ronique Dupierris, Anne-Marie Hesse, Jean-Philippe Menetrey, David Bouyssi , Thomas Burger, Yohann Cout , and Christophe Bruley5. Integrating Identification and Quantification Uncertainty for Differential Protein Abundance Analysis with TriqlerMatthew The and Lukas K ll6. Left-Censored Missing Value Imputation Approach for MS-Based Proteomics Data with GsimpRunmin Wei and Jingye Wang7. Towards a More Accurate Differential Analysis of Multiple Imputed Proteomics Data with mi4limmaMarie Chion, Christine Carapito, and Fr d ric Bertrand8. Uncertainty Aware Protein-Level Quantification and Differential Expression Analysis of Proteomics Data with seaMassAlexander M. Phillips, Richard D. Unwin, Simon J. Hubbard, and Andrew W. Dowsey9. Statistical Analysis of Quantitative Peptidomics and Peptide-Level Proteomics Data with ProstarMarianne Tardif, Enora Fremy, Anne-Marie Hesse, Thomas Burger, Yohann Cout , and Samuel Wieczorek10. msmsEDA and msmsTests: Label-Free Differential Expression by Spectral CountsJosep Gregori, lex S nchez, and Josep Villanueva11. Exploring Protein Interactome Data with IPinquiry: Statistical Analysis and Data Visualization by Spectral CountsLauriane Kuhn, Timoth e Vincent, Philippe Hammann, and H l ne Zuber12. Statistical Analysis of Post-Translational Modifications Quantified by Label-Free Proteomics Across Multiple Biological Conditions with R: Illustration from SARS-CoV-2 Infected CellsQuentin Giai Gianetto13. Fast, Free, and Flexible Peptide and Protein Quantification with FlashLFQRobert J. Millikin, Michael R. Shortreed, Mark Scalf, and Lloyd M. Smith14. Robust Prediction and Protein Selection with Adaptive PENSEDavid Kepplinger and Gabriela V. Cohen Freue15. Multivariate Analysis with the R Package mixOmicsZoe Welham, S bastien D jean, and Kim-Anh L Cao16. Inte", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/1/07/161/966/1071619667_b.jpg", "price_data" : { "retail_price" : "249.99", "online_price" : "249.99", "our_price" : "249.99", "club_price" : "249.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Statistical Analysis of Proteomic Data|Thomas Burger

Statistical Analysis of Proteomic Data : Methods and Tools

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Overview

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

V ronique Dupierris, Anne-Marie Hesse, Jean-Philippe Menetrey, David Bouyssi , Thomas Burger, Yohann Cout , and Christophe Bruley

5. Integrating Identification and Quantification Uncertainty for Differential Protein Abundance Analysis with Triqler

Matthew The and Lukas K ll

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 Fr d ric 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 Cout , and Samuel Wieczorek

10. msmsEDA and msmsTests: Label-Free Differential Expression by Spectral Counts

Josep Gregori, lex S nchez, and Josep Villanueva

11. Exploring Protein Interactome Data with IPinquiry: Statistical Analysis and Data Visualization by Spectral Counts

Lauriane Kuhn, Timoth e Vincent, Philippe Hammann, and H l ne 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, S bastien D jean, and Kim-Anh L Cao

16. Inte

This item is Non-Returnable

Details

  • ISBN-13: 9781071619667
  • ISBN-10: 1071619667
  • Publisher: Humana
  • Publish Date: October 2022
  • Dimensions: 10 x 7 x 0.94 inches
  • Shipping Weight: 2.03 pounds
  • Page Count: 393

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