Including transcription factor information in the superparamagnetic clustering of microarray data
Rosu Barbus, Haret-Codratian
In this work, we modify the superparamagnetic clustering algorithm (SPC) by adding an extra weight to the interaction formula that considers which genes are regulated by the same transcription factor. With this modiﬁed algorithm that we call SPCTF, we analyze Spellman et al. microarray data for cell cycle genes in yeast, and ﬁnd clusters with a higher number of elements compared with those obtained with the SPC algorithm. Some of the incorporated genes by using SPCFT were not detected at ﬁrst by Spellman et al. but were later identiﬁed by other studies, whereas several genes still remain unclassiﬁed. The clusters composed by unidentiﬁed genes were analyzed with MUSA, the motif ﬁnding using an unsupervised approach algorithm, and this allow us to select the clusters whose elements contain cell cycle transcription factor binding sites as clusters worth of further experimental studies because they would probably lead to new cell cycle genes. Finally, our idea of introducing available infor-mation about transcription factors to optimize the gene classiﬁcation could be implemented for other distance-based clustering algorithms.
Fecha de publicación2010
Palabras claveSuperparamagnetic clustering
Cell cycle genes
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