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Consensus clustering and functional interpretation of gene-expression data.
Abstract
Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in assigning related gene-expression profiles to clusters. Obtaining a consensus set of clusters from a number of clustering methods should improve confidence in gene-expression analysis. Here we introduce consensus clustering, which provides such an advantage. When coupled with a statistically based gene functional analysis, our method allowed the identification of novel genes regulated by NFkappaB and the unfolded protein response in certain B-cell lymphomas- Journal Article
- Cluster Analysis
- Computer Simulation
- Consensus Sequence
- Gene Expression Profiling
- Gene Expression Regulation
- Microarray Analysis
- Models, Genetic
- Microarray Analysis
- Cluster Analysis
- Gene Expression Profiling
- Gene Expression Regulation
- Consensus Sequence
- Models, Genetic
- Computer Simulation
- Cluster Analysis
- Computer Simulation
- Consensus Sequence
- Gene Expression Profiling
- Gene Expression Regulation
- Microarray Analysis
- Models, Genetic
- 05 Environmental Sciences
- 06 Biological Sciences
- 08 Information And Computing Sciences
- Bioinformatics