Dynamically weighted clustering with noise set
Open Access
- 9 December 2009
- journal article
- research article
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 26 (3), 341-347
- https://doi.org/10.1093/bioinformatics/btp671
Abstract
Motivation: Various clustering methods have been applied to microarray gene expression data for identifying genes with similar expression profiles. As the biological annotation data accumulated, more and more genes have been organized into functional categories. Functionally related genes may be regulated by common cellular signals, thus likely to be co-expressed. Consequently, utilizing the rapidly increasing functional annotation resources such as Gene Ontology (GO) to improve the performance of clustering methods is of great interest. On the opposite side of clustering, there are genes that have distinct expression profiles and do not co-express with other genes. Identification of these scattered genes could enhance the performance of clustering methods. Results: We developed a new clustering algorithm, Dynamically Weighted Clustering with Noise set (DWCN), which makes use of gene annotation information and allows for a set of scattered genes, the noise set, to be left out of the main clusters. We tested the DWCN method and contrasted its results with those obtained using several common clustering techniques on a simulated dataset as well as on two public datasets: the Stanford yeast cell-cycle gene expression data, and a gene expression dataset for a group of genetically different yeast segregants. Conclusion: Our method produces clusters with more consistent functional annotations and more coherent expression patterns than existing clustering techniques. Contact: yshen@stat.ucla.edu Supplementary information: Supplementary data are available at Bioinformatics online.Keywords
This publication has 37 references indexed in Scilit:
- Patterns of co-expression for protein complexes by size in Saccharomyces cerevisiaeNucleic Acids Research, 2008
- Discovering multi–level structures in bio-molecular data through the Bernstein inequalityBMC Bioinformatics, 2008
- Penalized and weightedK-means for clustering with scattered objects and prior information in high-throughput biological dataBioinformatics, 2007
- Model order selection for bio-molecular data clusteringBMC Bioinformatics, 2007
- Cluster Validation by Prediction StrengthJournal of Computational and Graphical Statistics, 2005
- Tight Clustering: A Resampling‐Based Approach for Identifying Stable and Tight Patterns in DataBiometrics, 2005
- Module networks: identifying regulatory modules and their condition-specific regulators from gene expression dataNature Genetics, 2003
- The random subspace method for constructing decision forestsIeee Transactions On Pattern Analysis and Machine Intelligence, 1998
- Comparing partitionsJournal of Classification, 1985
- Objective Criteria for the Evaluation of Clustering MethodsJournal of the American Statistical Association, 1971