Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry
暫譯: 質譜數據的蛋白質組學、代謝組學和脂質組學的統計分析

Datta, Susmita, Mertens, Bart J. a.

Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry

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商品描述

This book presents an overview of computational and statistical design and analysis of mass spectrometry-based proteomics, metabolomics, and lipidomics data. This contributed volume provides an introduction to the special aspects of statistical design and analysis with mass spectrometry data for the new omic sciences. The text discusses common aspects of design and analysis between and across all (or most) forms of mass spectrometry, while also providing special examples of application with the most common forms of mass spectrometry. Also covered are applications of computational mass spectrometry not only in clinical study but also in the interpretation of omics data in plant biology studies.

Omics research fields are expected to revolutionize biomolecular research by the ability to simultaneously profile many compounds within either patient blood, urine, tissue, or other biological samples. Mass spectrometry is one of the key analytical techniques used in these new omic sciences. Liquid chromatography mass spectrometry, time-of-flight data, and Fourier transform mass spectrometry are but a selection of the measurement platforms available to the modern analyst. Thus in practical proteomics or metabolomics, researchers will not only be confronted with new high dimensional data types-as opposed to the familiar data structures in more classical genomics-but also with great variation between distinct types of mass spectral measurements derived from different platforms, which may complicate analyses, comparison, and interpretation of results.


商品描述(中文翻譯)

這本書概述了基於質譜的蛋白質組學、代謝組學和脂質組學數據的計算和統計設計與分析。這本貢獻集提供了對質譜數據在新興組學科學中特殊統計設計和分析方面的介紹。文本討論了所有（或大多數）質譜形式之間及其內部的設計和分析的共同方面，同時也提供了最常見的質譜形式的應用特例。此外，還涵蓋了計算質譜在臨床研究中的應用，以及在植物生物學研究中對組學數據的解釋。

組學研究領域預期將通過同時分析患者血液、尿液、組織或其他生物樣本中的多種化合物來徹底改變生物分子研究。質譜是這些新興組學科學中使用的關鍵分析技術之一。液相色譜質譜、飛行時間數據和傅立葉變換質譜僅是現代分析師可用的測量平台的一部分。因此，在實際的蛋白質組學或代謝組學中，研究人員不僅會面臨新的高維數據類型——與更傳統的基因組學中的熟悉數據結構相對——還會面臨來自不同平台的不同質譜測量類型之間的巨大變異，這可能會使分析、比較和結果解釋變得複雜。

作者簡介

Susmita Datta received her PhD in statistics from the University of Georgia. She is a tenured professor in the Department of Biostatistics at the University of Florida. Before joining the University of Florida she was a professor in the Department of Bioinformatics and Biostatistics and a distinguished university scholar at the University of Louisville. She is a Fellow of the American Association for the Advancement of Science (AAAS), American Statistical Association (ASA), and an elected member of the International Statistical Institute (ISI). Her research interests include bioinformatics, genomics, proteomics, clustering and classification techniques, infectious disease modeling, statistical issues in population biology, systems biology, survival analysis, and multi state models. She is past president of the Caucus for Women in Statistics, and she actively supports research and education for women in STEM fields.

Bart Mertens received his PhD in statistical sciences from University College London, Department of Statistical Sciences, on statistical analysis methods for spectrometry data. He is currently Associate Professor at the Department of Medical Statistics and Bioinformatics of the Leiden University Medical Centre, where he has been working in both research and consulting for statistical analysis methodology with mass spectrometry proteomic data for more than 10 years.

作者簡介(中文翻譯)

Susmita Datta 於喬治亞大學獲得統計學博士學位。她是佛羅里達大學生物統計學系的終身教授。在加入佛羅里達大學之前，她曾是路易斯維爾大學生物資訊學與生物統計學系的教授及傑出大學學者。她是美國科學促進會（AAAS）、美國統計學會（ASA）的會士，並且是國際統計學會（ISI）的當選成員。她的研究興趣包括生物資訊學、基因組學、蛋白質組學、聚類與分類技術、傳染病建模、人口生物學中的統計問題、系統生物學、生存分析及多狀態模型。她曾擔任女性統計學者小組的主席，並積極支持女性在STEM領域的研究與教育。

Bart Mertens 於倫敦大學學院統計科學系獲得統計科學博士學位，研究主題為光譜數據的統計分析方法。他目前是萊頓大學醫學中心醫療統計與生物資訊系的副教授，並在質譜蛋白質組數據的統計分析方法研究與諮詢方面工作超過10年。

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