| Statistics in experimental design, preprocessing, and analysis of proteomics data. | |
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MedLine Citation:
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PMID: 21063953 Owner: NLM Status: In-Process |
Abstract/OtherAbstract:
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High-throughput experiments in proteomics, such as 2-dimensional gel electrophoresis (2-DE) and mass spectrometry (MS), yield usually high-dimensional data sets of expression values for hundreds or thousands of proteins which are, however, observed on only a relatively small number of biological samples. Statistical methods for the planning and analysis of experiments are important to avoid false conclusions and to receive tenable results. In this chapter, the most frequent experimental designs for proteomics experiments are illustrated. In particular, focus is put on studies for the detection of differentially regulated proteins. Furthermore, issues of sample size planning, statistical analysis of expression levels as well as methods for data preprocessing are covered. |
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Authors:
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Klaus Jung |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Methods in molecular biology (Clifton, N.J.) Volume: 696 ISSN: 1940-6029 ISO Abbreviation: Methods Mol. Biol. Publication Date: 2011 |
Date Detail:
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Created Date: 2010-11-10 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 9214969 Medline TA: Methods Mol Biol Country: United States |
Other Details:
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Languages: eng Pagination: 259-72 Citation Subset: IM |
Affiliation:
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Department of Medical Statistics, Georg-August-University Göttingen, Göttingen, Germany. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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