WMLRR: A Weighted Multi-View Low Rank Representation to Identify Cancer Subtypes From Multiple Types of Omics Data.

The identification of cancer subtypes is of great importance for understanding the heterogeneity of tumors and providing patients with more accurate diagnoses and treatments. However, it is still a challenge to effectively integrate multiple omics data to establish cancer subtypes. In this paper, we propose an unsupervised integration method, named weighted multi-view low rank representation (WMLRR), to identify cancer subtypes from multiple types of omics data. Given a group of patients described by multiple omics data matrices, we first learn a unified affinity matrix which encodes the similarities among patients by exploring the sparsity-consistent low-rank representations from the joint decompositions of multiple omics data matrices. Unlike existing subtype identification methods that treat each omics data matrix equally, we assign a weight to each omics data matrix and learn these weights automatically through the optimization process. Finally, we apply spectral clustering on the learned affinity matrix to identify cancer subtypes. Experiment results show that the survival times between our identified cancer subtypes are significantly different, and our predicted survivals are more accurate than other state-of-the-art methods. In addition, some clinical analyses of the diseases also demonstrate the effectiveness of our method in identifying molecular subtypes with biological significance and clinical relevance.

LRSK: a low-rank self-representation K-means method for clustering single-cell RNA-sequencing data.

The development of single-cell RNA-sequencing (scRNA-seq) technologies brings tremendous opportunities for quantitative research and analyses at the cellular level. In particular, as a crucial task of scRNA-seq analysis, single cell clustering shines a light on natural groupings of cells to give new insights into the biological mechanisms and disease studies. However, it remains a challenge to identify cell clusters from lots of cell mixtures effectively and accurately. In this paper, we propose a novel adaptive joint clustering framework, named the low-rank self-representation K-means method (LRSK), to learn the data representation matrix and cluster indicator matrix jointly from scRNA-seq data. Specifically, instead of calculating the similarities among cells from the original data, we seek a low-rank representation of the original data to better reflect the underlying relationships among cells. Moreover, an Augmented Lagrangian Multiplier (ALM) based optimization algorithm is adopted to solve this problem. Experimental results on various scRNA-seq datasets and case studies demonstrate that our method performs better than other state-of-the-art single cell clustering algorithms. The analysis of unlabeled large single-cell liver cancer sequencing data further shows that our prediction results are more reasonable and interpretable.