AP-Based Consensus Clustering for Gene Expression Time Series

We propose an unsupervised approach for analyzing gene time-series datasets. Our method combines Affinity Propagation (AP) and the spirit of consensus clustering-- extracting multiple partitions from different time intervals. Without priori knowledge of total number of clusters and exemplars, this method holds the relationship between genes through different time intervals, and eliminates the influence from noises and outliers. We demonstrate our method with both synthetic and real gene expression datasets showing significant improvement in accuracy and efficiency.