Hongfu Liu1, Rui Zhao2,3, Hongsheng Fang2,4, Feixiong Cheng5,6, Yun Fu1,7, Yang-Yu Liu2,6. 1. Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA. 2. Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA. 3. School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA. 4. Department of Statistics, Stanford University, Stanford, CA 94305, USA. 5. Center for Complex Network Research and Department of Physics, Northeastern University, Boston, MA 02115, USA. 6. Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA. 7. College of Computer and Information Science, Northeastern University, Boston, MA 02115, USA.
Abstract
MOTIVATION: Patient stratification or disease subtyping is crucial for precision medicine and personalized treatment of complex diseases. The increasing availability of high-throughput molecular data provides a great opportunity for patient stratification. Many clustering methods have been employed to tackle this problem in a purely data-driven manner. Yet, existing methods leveraging high-throughput molecular data often suffers from various limitations, e.g. noise, data heterogeneity, high dimensionality or poor interpretability. RESULTS: Here we introduced an Entropy-based Consensus Clustering (ECC) method that overcomes those limitations all together. Our ECC method employs an entropy-based utility function to fuse many basic partitions to a consensus one that agrees with the basic ones as much as possible. Maximizing the utility function in ECC has a much more meaningful interpretation than any other consensus clustering methods. Moreover, we exactly map the complex utility maximization problem to the classic K -means clustering problem, which can then be efficiently solved with linear time and space complexity. Our ECC method can also naturally integrate multiple molecular data types measured from the same set of subjects, and easily handle missing values without any imputation. We applied ECC to 110 synthetic and 48 real datasets, including 35 cancer gene expression benchmark datasets and 13 cancer types with four molecular data types from The Cancer Genome Atlas. We found that ECC shows superior performance against existing clustering methods. Our results clearly demonstrate the power of ECC in clinically relevant patient stratification. AVAILABILITY AND IMPLEMENTATION: The Matlab package is available at http://scholar.harvard.edu/yyl/ecc . CONTACT: yunfu@ece.neu.edu or yyl@channing.harvard.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Patient stratification or disease subtyping is crucial for precision medicine and personalized treatment of complex diseases. The increasing availability of high-throughput molecular data provides a great opportunity for patient stratification. Many clustering methods have been employed to tackle this problem in a purely data-driven manner. Yet, existing methods leveraging high-throughput molecular data often suffers from various limitations, e.g. noise, data heterogeneity, high dimensionality or poor interpretability. RESULTS: Here we introduced an Entropy-based Consensus Clustering (ECC) method that overcomes those limitations all together. Our ECC method employs an entropy-based utility function to fuse many basic partitions to a consensus one that agrees with the basic ones as much as possible. Maximizing the utility function in ECC has a much more meaningful interpretation than any other consensus clustering methods. Moreover, we exactly map the complex utility maximization problem to the classic K -means clustering problem, which can then be efficiently solved with linear time and space complexity. Our ECC method can also naturally integrate multiple molecular data types measured from the same set of subjects, and easily handle missing values without any imputation. We applied ECC to 110 synthetic and 48 real datasets, including 35 cancer gene expression benchmark datasets and 13 cancer types with four molecular data types from The Cancer Genome Atlas. We found that ECC shows superior performance against existing clustering methods. Our results clearly demonstrate the power of ECC in clinically relevant patient stratification. AVAILABILITY AND IMPLEMENTATION: The Matlab package is available at http://scholar.harvard.edu/yyl/ecc . CONTACT: yunfu@ece.neu.edu or yyl@channing.harvard.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.