Brief. Bioinformatics - Accounting for noise when clustering biological data.

Tópicos

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Resumo

Clustering is a powerful and commonly used technique that organizes and elucidates the structure of biological data. Clustering data from gene expression, metabolomics and proteomics experiments has proven to be useful at deriving a variety of insights, such as the shared regulation or function of biochemical components within networks. However, experimental measurements of biological processes are subject to substantial noise-stemming from both technical and biological variability-and most clustering algorithms are sensitive to this noise. In this article, we explore several methods of accounting for noise when analyzing biological data sets through clustering. Using a toy data set and two different case studies-gene expression and protein phosphorylation-we demonstrate the sensitivity of clustering algorithms to noise. Several methods of accounting for this noise can be used to establish when clustering results can be trusted. These methods span a range of assumptions about the statistical properties of the noise and can therefore be applied to virtually any biological data source.

Resumo Limpo

cluster power common use techniqu organ elucid structur biolog data cluster data gene express metabolom proteom experi proven use deriv varieti insight share regul function biochem compon within network howev experiment measur biolog process subject substanti noisestem technic biolog variabilityand cluster algorithm sensit nois articl explor sever method account nois analyz biolog data set cluster use toy data set two differ case studiesgen express protein phosphorylationw demonstr sensit cluster algorithm nois sever method account nois can use establish cluster result can trust method span rang assumpt statist properti nois can therefor appli virtual biolog data sourc

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