Abstract
It is a vital task to design an integrated machine learning model to discover cancer subtypes and understand the heterogeneity of cancer based on multiple omics data. In recent years, some multi-view clustering algorithms have been proposed and applied to the prediction of cancer subtypes. Among them, the multi-view clustering methods based on graph learning are widely concerned. These multi-view approaches usually have one or more of the following problems. Many multi-view algorithms use the original omics data matrix to construct the similarity matrix and ignore the learning of the similarity matrix. They separate the data clustering process from the graph learning process, resulting in a highly dependent clustering performance on the predefined graph. In the process of graph fusion, these methods simply take the average value of the affinity graph of multiple views to represent the result of the fusion graph, and the rich heterogeneous information is not fully utilized. To solve the above problems, in this paper, a Multi-view Spectral Clustering Based on Multi-smooth Representation Fusion (MRF-MSC) method was proposed. Firstly, MRF-MSC constructs a smooth representation for each data type, which can be viewed as a sample (patient) similarity matrix. The smooth representation can explicitly enhance the grouping effect. Secondly, MRF-MSC integrates the smooth representation of multiple omics data to form a similarity matrix containing all biological data information through graph fusion. In addition, MRF-MSC adaptively gives weight factors to the smooth regularization representation of each omics data by using the self-weighting method. Finally, MRF-MSC imposes constrained Laplacian rank on the fusion similarity matrix to get a better cluster structure. The above problems can be transformed into spectral clustering for solving, and the clustering results can be obtained. MRF-MSC unifies the above process of graph construction, graph fusion and spectral clustering under one framework, which can learn better data representation and high-quality graphs, so as to achieve better clustering effect. In the experiment, MRF-MSC obtained good experimental results on the TCGA cancer data sets.
Highlights
Cancer is a malignant and heterogeneous disease caused by changes in cellular and molecular expression, epigenetics, transcription, and proteome levels (Burrell et al, 2013)
In order to solve the problem of data preprocessing, many classical dimensionality reduction techniques are applied to the proposed clustering algorithms, e.g., Principal Component Analysis (PCA; Ding and He, 2004), Non-negative Matrix Factorization (NMF; Zhang et al, 2012), etc
In order to prove the effectiveness of the MRF-MSC algorithm in cancer subtype prediction, we applied MRF-MSC to the cancer multi-omics data downloaded and preprocessed from The Cancer Genome Atlas (TCGA) by Wang et al (2014) and Rappoport and Shamir (2018)
Summary
Cancer is a malignant and heterogeneous disease caused by changes in cellular and molecular expression, epigenetics, transcription, and proteome levels (Burrell et al, 2013). ICluster (Shen et al, 2010) is a Gaussian hidden variable model, and its extended version, iClusterPluse (Mo et al, 2013), is an effective and classical multi-omics data clustering method. It considers that different variable types follow different linear probability relationships, and constructs a joint sparse model to complete feature selecting and sample clustering tasks. PFA uses the dynamic collimation algorithm to achieve the fusion of feature space
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
