ArticleBMC medical genomics2017
Subtype identification from heterogeneous TCGA datasets on a genomic scale by multi-view clustering with enhanced consensus.
Article in BMC medical genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- COPS: A novel platform for multi-omic disease subtype discovery via robust multi-objective evaluation of clustering algorithms.PLoS computational biology · 2024Article
- Characterization of Expression-Based Gene Clusters Gives Insights into Variation in Patient Response to Cancer Therapies.Cancer informatics · 2024Article
- Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model.PLoS computational biology · 2022Article
- Identification of specific microRNA-messenger RNA regulation pairs in four subtypes of breast cancer.IET systems biology · 2020Article
- Integrative subspace clustering by common and specific decomposition for applications on cancer subtype identification.BMC medical genomics · 2019Article
- A bioinformatics potpourri.BMC genomics · 2018Article
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2 authors.
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Abstract
backgroundThe Cancer Genome Atlas (TCGA) has collected transcriptome, genome and epigenome information for over 20 cancers from thousands of patients. The availability of these diverse data types makes it necessary to combine these data to capture the heterogeneity of biological processes and phenotypes and further identify homogeneous subtypes for cancers such as breast cancer. Many multi-view clustering approaches are proposed to discover clusters across different data types. The problem is challenging when different data types show poor agreement of clustering structure.
resultsIn this work, we first propose a multi-view clustering approach with consensus (CMC), which tries to find consensus kernels among views by using Hilbert Schmidt Independence Criterion. To tackle the problem when poor agreement among views exists, we further propose a multi-view clustering approach with enhanced consensus (ECMC) to solve this problem by decomposing the kernel information in each view into a consensus part and a disagreement part. The consensus parts for different views are supposed to be similar, and the disagreement parts should be independent with the consensus parts. Both the CMC and ECMC models can be solved by alternative updating with semi-definite programming. Our experiments on both simulation datasets and real-world benchmark datasets show that ECMC model could achieve higher clustering accuracies than other state-of-art multi-view clustering approaches. We also apply the ECMC model to integrate mRNA expression, DNA methylation and microRNA (miRNA) expression data for five cancer data sets, and the survival analysis show that our ECMC model outperforms other methods when identifying cancer subtypes. By Fisher's combination test method, we found that three computed subtypes roughly correspond to three known breast cancer subtypes including luminal B, HER2 and basal-like subtypes.
conclusionIntegrating heterogeneous TCGA datasets by our proposed multi-view clustering approach ECMC could effectively identify cancer subtypes.
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