Multi-view spectral clustering with latent representation learning for applications on multi-omics cancer subtyping

Abstract

Driven by multi-omics data, some multi-view clustering algorithms have been successfully applied to cancer subtypes prediction, aiming to identify subtypes with biometric differences in the same cancer, thereby improving the clinical prognosis of patients and designing personalized treatment plan. Due to the fact that the number of patients in omics data is much smaller than the number of genes, multi-view spectral clustering based on similarity learning has been widely developed. However, these algorithms still suffer some problems, such as over-reliance on the quality of pre-defined similarity matrices for clustering results, inability to reasonably handle noise and redundant information in high-dimensional omics data, ignoring complementary information between omics data, etc. This paper proposes multi-view spectral clustering with latent representation learning (MSCLRL) method to alleviate the above problems. First, MSCLRL generates a corresponding low-dimensional latent representation for each omics data, which can effectively retain the unique information of each omics and improve the robustness and accuracy of the similarity matrix. Second, the obtained latent representations are assigned appropriate weights by MSCLRL, and global similarity learning is performed to generate an integrated similarity matrix. Third, the integrated similarity matrix is used to feed back and update the low-dimensional representation of each omics. Finally, the final integrated similarity matrix is used for clustering. In 10 benchmark multi-omics datasets and 2 separate cancer case studies, the experiments confirmed that the proposed method obtained statistically and biologically meaningful cancer subtypes.

scGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data

“…To better cluster cells by exploiting more information from single cells, a number of methods [ 24 – 30 ] have been proposed for analyzing single-cell multi-omics data, e.g. scRNA-seq, single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq), DNA methylation measurements, and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq).…”

“…scMCs [ 27 ] co-models single-cell transcriptome and epigenetic data to get omics-specific and consistent representations, and fuse them into a common embedded representation. In addition, MSCLRL [ 30 ] is able to automatically learn latent representations from multi-omics data for cancer subtyping.…”