Consensus clustering improves the separation signals. Results are shown for two embryonic development datasets: Yan (A) and Deng (B). We use consensus clustering from FEAST and K-means to determine initial clusters. Then, we calculate the feature significance by F-test. The results demonstrate that the P-values from the consensus clustering are more significant.