Statistical Analysis of Proteomic Data

Thomas Burger

ISBN 10: 1071619667 / ISBN 13: 9781071619667

Editore: Springer US, Springer US Okt 2022, 2022

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Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 408 pp. Englisch.

Codice articolo 9781071619667

Dati bibliografici

• Titolo: Statistical Analysis of Proteomic Data
• Editore: Springer US, Springer US Okt 2022
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 1071619667
• ISBN 13: 9781071619667
• Rilegatura: Buch
• Condizione: Neu

Descrizione articolo

Riassunto

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.