Document Detail


Statistics in experimental design, preprocessing, and analysis of proteomics data.
MedLine Citation:
PMID:  21063953     Owner:  NLM     Status:  In-Process    
Abstract/OtherAbstract:
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.
Authors:
Klaus Jung
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Methods in molecular biology (Clifton, N.J.)     Volume:  696     ISSN:  1940-6029     ISO Abbreviation:  Methods Mol. Biol.     Publication Date:  2011  
Date Detail:
Created Date:  2010-11-10     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9214969     Medline TA:  Methods Mol Biol     Country:  United States    
Other Details:
Languages:  eng     Pagination:  259-72     Citation Subset:  IM    
Affiliation:
Department of Medical Statistics, Georg-August-University Göttingen, Göttingen, Germany.
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