Double weighted ensemble clustering for cancer subtypes analysis

The era of big data provides the possibility of precision medicine. The most important idea we have for cancer is to divide and treat. Theoretically, each person's cancer should be different, so it is very necessary to make personalized treatment plans for different cancer patients. Subtype analysis of cancer can be viewed as a clustering problem, while ensemble clustering techniques are widely followed for their ability to combine multiple basic clusters into potentially better and more robust clusters. However, the reliability of the present ensemble clustering methods in cancer subtype analysis still needs to be improved. Therefore, we propose a double weighted ensemble clustering method (DWEC), which first derives the similarity matrix of each base cluster based on the local weighting method, and this process can be regarded as the first weighting based on clusters. Subsequently, the objective of finding the final partitions is regarded as an optimization problem, and the similarity matrix corresponding to each base cluster is weighted twice by the block coordinate descent algorithm to solve the optimal partitions result. The best experimental results were obtained in both labeled datasets and unlabeled cancer gene datasets, validating the superiority of the method. For cancer subtype analysis, although our proposed method did not show statistically significant differences in survival distributions of several subtypes in the subtype analysis of glioblastoma multiforme. However, it performed best in the results of the temporal test for all other four cancer gene data, and therefore, we conclude that our method is more effective for cancer subtype analysis compared with other methods.