Enrichment constrained time-dependent clustering analysis for finding meaningful temporal transcription modules

AUTOR(ES)

Meng, Jia

FONTE

Oxford University Press

RESUMO

Motivation: Clustering is a popular data exploration technique widely used in microarray data analysis. When dealing with time-series data, most conventional clustering algorithms, however, either use one-way clustering methods, which fail to consider the heterogeneity of temporary domain, or use two-way clustering methods that do not take into account the time dependency between samples, thus producing less informative results. Furthermore, enrichment analysis is often performed independent of and after clustering and such practice, though capable of revealing biological significant clusters, cannot guide the clustering to produce biologically significant result.

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