Subtype-WESLR: identifying cancer subtype with weighted ensemble sparse latent representation of multi-view data

The discovery of cancer subtypes has become much-researched topic in oncology. Dividing cancer patients into subtypes can provide personalized treatments for heterogeneous patients. High-throughput technologies provide multiple omics data for cancer subtyping. Integration of multi-view data is used to identify cancer subtypes in many computational methods, which obtain different subtypes for the same cancer, even using the same multi-omics data. To a certain extent, these subtypes from distinct methods are related, which may have certain guiding significance for cancer subtyping. It is a challenge to effectively utilize the valuable information of distinct subtypes to produce more accurate and reliable subtypes. A weighted ensemble sparse latent representation (subtype-WESLR) is proposed to detect cancer subtypes on heterogeneous omics data. Using a weighted ensemble strategy to fuse base clustering obtained by distinct methods as prior knowledge, subtype-WESLR projects each sample feature profile from each data type to a common latent subspace while maintaining the local structure of the original sample feature space and consistency with the weighted ensemble and optimizes the common subspace by an iterative method to identify cancer subtypes. We conduct experiments on various synthetic datasets and eight public multi-view datasets from The Cancer Genome Atlas. The results demonstrate that subtype-WESLR is better than competing methods by utilizing the integration of base clustering of exist methods for more precise subtypes.

Automated summary

The discovery of cancer subtypes through high-throughput technologies and multi-view data integration methods has become a significant research topic in oncology. By utilizing weighted ensemble sparse latent representation, researchers are able to identify cancer subtypes more accurately and reliably, demonstrating superiority over competing methods.