Standard clustering methods can classify genes successfully when applied to relatively small data sets, but have limited use in the analysis of large-scale expression data, mainly owing to their assignment of a gene to a single cluster. Here we propose an alternative method for the global analysis of genome-wide expression data. Our approach assigns genes to context-dependent and potentially overlapping 'transcription modules', thus overcoming the main limitations of traditional clustering methods. We use our method to elucidate regulatory properties of cellular pathways and to characterize cis-regulatory elements. By applying our algorithm systematically to all of the available expression data on Saccharomyces cerevisiae, we identify a comprehensive set of overlapping transcriptional modules. Our results provide functional predictions for numerous genes, identify relations between modules and present a global view on the transcriptional network.
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We thank D.R. Kellogg for the sda2-1 strain; U. Alon, M. Dolev, E. Domany, A. Eldar, O. Gileadi, Y. Kafri, B.-Z. Shilo and S. Shnider for discussions and comments on the manuscript; G. Jona and O. Gileadi for experimental help. This work was supported by the US National Institutes of Health, the Israeli Science Ministry and the Benoziyo center. S.B. is a Koshland fellow. N.B. is the incumbent of the Soretta and Henry Shapiro career development chair.
The authors declare no competing financial interests.
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