Multiview clustering of multi-omics data integration by using a penalty model

Methods for the multiview clustering and integration of multi-omics data have been developed recently to solve problems caused by data noise or limited sample size and to integrate multi-omics data with consistent (common) and differential cluster patterns. However, the integration of such data still suffers from limited performance and low accuracy.

In this study, a computational framework for the multiview clustering method based on the penalty model is presented to overcome the challenges of low accuracy and limited performance in the case of integrating multi-omics data with consistent (common) and differential cluster patterns. The performance of the proposed method was evaluated on synthetic data and four real multi-omics data and then compared with approaches presented in the literature under different scenarios. Result implies that our method exhibits competitive performance compared with recently developed techniques when the underlying clusters are consistent with synthetic data. In the case of the differential clusters, the proposed method also presents an enhanced performance. In addition, with regards to real omics data, the developed method exhibits better performance, demonstrating its ability to provide more detailed information within each data type and working better to integrate multi-omics data with consistent (common) and differential cluster patterns. This study shows that the proposed method offers more significant differences in survival times across all types of cancer.

A new multiview clustering method is proposed in this study based on synthetic and real data. This method performs better than other techniques previously presented in the literature in terms of integrating multi-omics data with consistent and differential cluster patterns and determining the significance of difference in survival times.