Accounting for noise when clustering biological data.
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.
Division of Biology and Biomedical Sciences, Washington University in St. Louis, One Brookings Drive, Brauer 2004, St. Louis, MO 63130, USA.
Roman
R
Sloutsky
Nicolas
N
Jimenez
S Joshua
SJ
Swamidass
Kristen M
KM
Naegle
D000465:Algorithms; D016000:Cluster Analysis; D015870:Gene Expression; D010766:Phosphorylation; D011506:Proteins / Q000737:chemistry*; D059467:Transcriptome
D011506:Proteins