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Statistical Analysis of Proteomic Data: Methods and Tools 2022 ed. [Kõva köide]

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  • Formaat: Hardback, 393 pages, kõrgus x laius: 254x178 mm, kaal: 961 g, 477 Illustrations, color; 50 Illustrations, black and white; XI, 393 p. 527 illus., 477 illus. in color., 1 Hardback
  • Sari: Methods in Molecular Biology 2426
  • Ilmumisaeg: 30-Oct-2022
  • Kirjastus: Springer-Verlag New York Inc.
  • ISBN-10: 1071619667
  • ISBN-13: 9781071619667
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Statistical Analysis of Proteomic Data: Methods and Tools 2022 ed.Suurem pilt
  • Formaat: Hardback, 393 pages, kõrgus x laius: 254x178 mm, kaal: 961 g, 477 Illustrations, color; 50 Illustrations, black and white; XI, 393 p. 527 illus., 477 illus. in color., 1 Hardback
  • Sari: Methods in Molecular Biology 2426
  • Ilmumisaeg: 30-Oct-2022
  • Kirjastus: Springer-Verlag New York Inc.
  • ISBN-10: 1071619667
  • ISBN-13: 9781071619667
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781071619667.html
Märksõnad:
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.
Unveiling the Links between Peptide Identification and Differential
Analysis FDR Controls by Means of a Practical Introduction to Knockoff
Filters.- A Pipeline for Peptide Detection Using Multiple Decoys.- Enhanced
Proteomic Data Analysis with MetaMorpheus.- Validation of MS/MS
Identifications and Label-Free Quantification Using Proline.- Integrating
Identification and Quantification Uncertainty for Differential Protein
Abundance Analysis with Triqler.- Left-Censored Missing Value Imputation
Approach for MS-Based Proteomics Data with Gsimp.- Towards a More Accurate
Differential Analysis of Multiple Imputed Proteomics Data with mi4limma.-
Uncertainty Aware Protein-Level Quantification and Differential Expression
Analysis of Proteomics Data with seaMass.- Statistical Analysis of
Quantitative Peptidomics and Peptide-Level Proteomics Data with Prostar.-
msmsEDA and msmsTests: Label-Free Differential Expression by Spectral
Counts.- Exploring Protein Interactome Data with IPinquiry: Statistical
Analysis and Data Visualization by Spectral Counts.- Statistical Analysis of
Post-Translational Modifications Quantified by Label-Free Proteomics Across
Multiple Biological Conditions with R: Illustration from SARS-CoV-2 Infected
Cells.- Fast, Free, and Flexible Peptide and Protein Quantification with
FlashLFQ.- Robust Prediction and Protein Selection with Adaptive PENSE.-
Multivariate Analysis with the R Package mixOmics.- Integrating Multiple
Quantitative Proteomic Analyses Using MetaMSD.- Application of WGCNA and
PloGO2 in the Analysis of Complex Proteomic Data.