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Accounting for noise when clustering biological data.
Roman Sloutsky1, Nicolas Jimenez, S Joshua Swamidass
1Division of Biology and Biomedical Sciences, Washington University in St. Louis, One Brookings Drive, Brauer 2004, St. Louis, MO 63130, USA.
Briefings in Bioinformatics
|October 16, 2012
Summary
This study explores noise in biological data clustering. We demonstrate how to assess and manage noise to ensure reliable clustering results for gene expression and protein phosphorylation data.
Area of Science:
- Bioinformatics
- Computational Biology
- Data Analysis
Background:
- Clustering organizes biological data (gene expression, metabolomics, proteomics) to reveal insights into biological networks.
- Experimental biological data is prone to noise from technical and biological variability, impacting clustering accuracy.
Purpose of the Study:
- To explore methods for accounting for noise in biological data clustering.
- To demonstrate the sensitivity of clustering algorithms to noise using case studies.
Main Methods:
- Utilized a toy dataset and two case studies: gene expression and protein phosphorylation.
- Evaluated several noise-handling techniques for clustering algorithms.
Main Results:
- Demonstrated the sensitivity of clustering algorithms to noise in biological datasets.
- Showcased methods to account for noise and establish trust in clustering outcomes.
Conclusions:
- Noise significantly affects biological data clustering.
- Employing noise-accounting methods enhances the reliability of clustering results across various biological data types.
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