Abstract:
:We compare several commonly used expression-based gene clustering algorithms using a figure of merit based on the mutual information between cluster membership and known gene attributes. By studying various publicly available expression data sets we conclude that enrichment of clusters for biological function is, in general, highest at rather low cluster numbers. As a measure of dissimilarity between the expression patterns of two genes, no method outperforms Euclidean distance for ratio-based measurements, or Pearson distance for non-ratio-based measurements at the optimal choice of cluster number. We show the self-organized-map approach to be best for both measurement types at higher numbers of clusters. Clusters of genes derived from single- and average-linkage hierarchical clustering tend to produce worse-than-random results.
journal_name
Genome Resjournal_title
Genome researchauthors
Gibbons FD,Roth FPdoi
10.1101/gr.397002subject
Has Abstractpub_date
2002-10-01 00:00:00pages
1574-81issue
10eissn
1088-9051issn
1549-5469journal_volume
12pub_type
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