Analysis of a Gibbs sampler method for model-based clustering of gene expression data.
BACKGROUND
Over the last decade, a large variety of clustering algorithms have been developed to detect coregulatory relationships among genes from microarray gene expression data. Model-based clustering approaches have emerged as statistically well-grounded methods, but the properties of these algorithms when applied to large-scale data sets are not always well understood. An in-depth analysis can reveal important insights about the performance of the algorithm, the expected quality of the output clusters, and the possibilities for extracting more relevant information out of a particular data set.


RESULTS
We have extended an existing algorithm for model-based clustering of genes to simultaneously cluster genes and conditions, and used three large compendia of gene expression data for Saccharomyces cerevisiae to analyze its properties. The algorithm uses a Bayesian approach and a Gibbs sampling procedure to iteratively update the cluster assignment of each gene and condition. For large-scale data sets, the posterior distribution is strongly peaked on a limited number of equiprobable clusterings. A GO annotation analysis shows that these local maxima are all biologically equally significant, and that simultaneously clustering genes and conditions performs better than only clustering genes and assuming independent conditions. A collection of distinct equivalent clusterings can be summarized as a weighted graph on the set of genes, from which we extract fuzzy, overlapping clusters using a graph spectral method. The cores of these fuzzy clusters contain tight sets of strongly coexpressed genes, while the overlaps exhibit relations between genes showing only partial coexpression.


BACKGROUND
GaneSh, a Java package for coclustering, is available under the terms of the GNU General Public License from our website at http://bioinformatics.psb.ugent.be/software
Joshi
Anagha
A
Department of Plant Systems Biology, VIB, Technologiepark 927, 9052 Gent, Belgium.
Van de Peer
Yves
Y
Michoel
Tom
T
D000465:Algorithms; D016000:Cluster Analysis; D005583:Fourier Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D005810:Multigene Family / Q000502:physiology*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*; D018401:Sample Size; D012815:Signal Processing, Computer-Assisted
D029701:Saccharomyces cerevisiae Proteins