Skip to Main Content
An algorithm for the discovery of time varying modules using genome-wide expression data is presented here. When applied to large-scale time serious data, our method is designed to discover not only the transcription modules but also their timing information, which is rarely annotated by the existing approaches. Rather than assuming commonly defined time constant transcription modules, a module is depicted as a set of genes that are co-regulated during a specific period of time, i.e., a time-dependent transcription module (TDTM). A rigorous mathematical definition of TDTM is provided, which serve as an objective function for the retrieving modules. Based on the definition, an effective signature algorithm is proposed that iteratively searches the transcription modules from the time series data. The proposed method was tested on the simulated systems and applied to the human time series microarray data derived from Kaposipsilas sarcoma-associated herpesvirus (KSHV) infection of human endothelial cells. The result has been verified by expression analysis systematic explorer.