Yeast Transcriptional Module Identification Based on Line Manifold

A living cell can carry out complex biological functions dependent on its transcriptional regulatory modules. Therefore, identifying transcriptional regulatory modules is very important for understanding cell function and its transcription mechanism. In this paper, a novel algorithm based on line manifold clusters and a linear mixture framework is presented for transcriptional regulatory module identification. A combined dissimilarity is constructed by combining gene expression and Chip-chip data with the linear mixture framework. Then the combined dissimilarity is introduced into a line manifold cluster searching algorithm. The application of this novel algorithm for two yeast gene expression datasets identified many transcriptional regulatory modules with significant biological meanings. The analysis of the identified transcriptional modules is helpful for a further understanding of yeast transcription mechanism.