Accounting for noise when clustering biological data

Roman Sloutsky, Nicolas Jimenez, S. Joshua Swamidass, Kristen M. Naegle

Research output: Contribution to journalArticle

13 Scopus citations


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.

Original languageEnglish
Article numberbbs057
Pages (from-to)423-436
Number of pages14
JournalBriefings in Bioinformatics
Issue number4
StatePublished - Jul 1 2013


  • Cluster ensemble
  • Clustering
  • Measurement variability
  • Noise
  • Random effects
  • Unsupervised learning

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