Overview
- Poses new challenges and calls for scalable solutions to the analysis of such high dimensional data
- Present the systematic and analytical approaches and strategies from both biostatistics and bioinformatics to the analysis of correlated and high-dimensional data
Part of the book series: Applied Bioinformatics and Biostatistics in Cancer Research (ABB)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
Keywords
About this book
Multivariate analysis is a mainstay of statistical tools in the analysis of biomedical data. It concerns with associating data matrices of n rows by p columns, with rows representing samples (or patients) and columns attributes of samples, to some response variables, e.g., patients outcome. Classically, the sample size n is much larger than p, the number of variables. The properties of statistical models have been mostly discussed under the assumption of fixed p and infinite n. The advance of biological sciences and technologies has revolutionized the process of investigations of cancer. The biomedical data collection has become more automatic and more extensive. We are in the era of p as a large fraction of n, and even much larger than n. Take proteomics as an example. Although proteomic techniques have been researched and developed for many decades to identify proteins or peptides uniquely associated with a given disease state, until recently this has been mostly a laborious process, carried out one protein at a time. The advent of high throughput proteome-wide technologies such as liquid chromatography-tandem mass spectroscopy make it possible to generate proteomic signatures that facilitate rapid development of new strategies for proteomics-based detection of disease. This poses new challenges and calls for scalable solutions to the analysis of such high dimensional data. In this volume, we will present the systematic and analytical approaches and strategies from both biostatistics and bioinformatics to the analysis of correlated and high-dimensional data.
Editors and Affiliations
Bibliographic Information
Book Title: High-Dimensional Data Analysis in Cancer Research
Editors: Xiaochun Li, Ronghui Xu
Series Title: Applied Bioinformatics and Biostatistics in Cancer Research
DOI: https://doi.org/10.1007/978-0-387-69765-9
Publisher: Springer New York, NY
eBook Packages: Biomedical and Life Sciences, Biomedical and Life Sciences (R0)
Copyright Information: Springer-Verlag New York 2009
Hardcover ISBN: 978-0-387-69763-5Published: 12 December 2008
Softcover ISBN: 978-1-4419-2414-8Published: 19 November 2010
eBook ISBN: 978-0-387-69765-9Published: 19 December 2008
Series ISSN: 2363-9644
Series E-ISSN: 2363-9652
Edition Number: 1
Number of Pages: VIII, 392
Number of Illustrations: 17 b/w illustrations, 6 illustrations in colour
Topics: Cancer Research, Laboratory Medicine, Human Genetics, Medical Microbiology, Molecular Medicine, Neurosciences