Statistical Analysis of Proteomic Data

Methods and Tools

AvThomas Burger

Häftad, Engelska, 2023

Del i serien Methods in Molecular Biology

1 694 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2023-10-30
Mått:178 x 254 x 23 mm
Vikt:764 g
Format:Häftad
Språk:Engelska
Serie:Methods in Molecular Biology
Antal sidor:393
Förlag:Springer-Verlag New York Inc.
ISBN:9781071619698

Utforska kategorier

Innehållsförteckning

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.