Finding explained groups of time-course gene expression profiles with predictive clustering trees.
In biology, analyzing time course data is usually a two-step process, beginning with clustering of similar temporal profiles. After the initial clustering, depending on the expert's knowledge, descriptions of the clusters are elucidated (e.g., Gene Ontology terms that are enriched in the clusters). In this paper, we investigate the application of so-called predictive clustering trees (PCTs) for the analysis of time series data. PCTs are a part of a more general framework of predictive clustering, which unifies clustering and prediction. Their advantage over usual clustering approaches is that they partition the time course data into homogeneous clusters while at the same time providing symbolic descriptions of the clusters. We evaluate our approach on multiple yeast microarray time series datasets. Each dataset records the change over time in the expression level of yeast genes as a response to a specific change in environmental conditions. We demonstrate that PCTs are able to cluster genes with similar temporal profiles, yield a predictive model of the temporal profiles of genes based on a cluster prototype, and provide cluster descriptions, all in a single step.
Slavkov
Ivica
I
Dept. of Knowledge Technologies, Jozef Stefan Institute, Slovenia. Ivica.Slavkov@ijs.si
Gjorgjioski
Valentin
V
Struyf
Jan
J
Dzeroski
Saso
S
D000465:Algorithms; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D016681:Genome, Fungal; D005810:Multigene Family; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D013312:Stress, Physiological; D049490:Systems Biology; D013997:Time Factors