Statistical Analysis of Proteomic Data

Methods and Tools

AvThomas Burger

Inbunden, Engelska, 2022

Del i serien Methods in Molecular Biology

2 347 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

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:2022-10-30
Mått:178 x 254 x 28 mm
Vikt:961 g
Format:Inbunden
Språk:Engelska
Serie:Methods in Molecular Biology
Antal sidor:393
Förlag:Springer-Verlag New York Inc.
ISBN:9781071619667

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.