Identifying Synchronous and Asynchronous Co-regulations from Time Series Gene Expression Data

The complexity of a biological system provides a great diversity of correlations among genes/gene clusters, including synchronous and asynchronous co-regulations, each of which can be further divided into two categories: activation and inhibition. Most existing methods can only identify the synchronous activation patterns, such as shifting, scaling and shifting-and-scaling, however, few focuses on capturing both synchronous and asynchronous co-regulations. In this paper, we propose a coding scheme, where two genes with the same code must be co-regulated. Based on the coding scheme, an efficient clustering algorithm is devised to simultaneously capture all known co-regulated relationships (synchronous and asynchronous) among genes/gene clusters. Furthermore, the detailed and complete co-regulation information, which facilitates the study of genetic regulatory networks, can be easily derived from the resulting clusters. Experiments from both real and synthetic microarray datasets prove the effectiveness and efficiency of our method.