Fast dimension reduction and integrative clustering of multi-omics data using low-rank approximation: application to cancer molecular classification

BMC Genomics

Methodology Article

Dingming Wu¹  Jin Gu¹  Dongfang Wang¹  Michael Q. Zhang² 

[1] MOE Key Laboratory of Bioinformatics, TNLIST Bioinformatics Division & Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, 100084, Beijing, China;MOE Key Laboratory of Bioinformatics, TNLIST Bioinformatics Division & Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, 100084, Beijing, China;Department of Biological Sciences, Center for Systems Biology, University of Texas at Dallas, 75080, Richardson, TX, USA;

关键词: Mutli-omics;    Cancer;    Low-rank approximation;    Clustering;    Dimension reduction;    Algorithm;   

DOI  :  10.1186/s12864-015-2223-8

 received in 2015-05-22, accepted in 2015-11-16,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundOne major goal of large-scale cancer omics study is to identify molecular subtypes for more accurate cancer diagnoses and treatments. To deal with high-dimensional cancer multi-omics data, a promising strategy is to find an effective low-dimensional subspace of the original data and then cluster cancer samples in the reduced subspace. However, due to data-type diversity and big data volume, few methods can integrative and efficiently find the principal low-dimensional manifold of the high-dimensional cancer multi-omics data.ResultsIn this study, we proposed a novel low-rank approximation based integrative probabilistic model to fast find the shared principal subspace across multiple data types: the convexity of the low-rank regularized likelihood function of the probabilistic model ensures efficient and stable model fitting. Candidate molecular subtypes can be identified by unsupervised clustering hundreds of cancer samples in the reduced low-dimensional subspace. On testing datasets, our method LRAcluster (low-rank approximation based multi-omics data clustering) runs much faster with better clustering performances than the existing method. Then, we applied LRAcluster on large-scale cancer multi-omics data from TCGA. The pan-cancer analysis results show that the cancers of different tissue origins are generally grouped as independent clusters, except squamous-like carcinomas. While the single cancer type analysis suggests that the omics data have different subtyping abilities for different cancer types.ConclusionsLRAcluster is a very useful method for fast dimension reduction and unsupervised clustering of large-scale multi-omics data. LRAcluster is implemented in R and freely available via http://bioinfo.au.tsinghua.edu.cn/software/lracluster/.

【 授权许可】

CC BY   
© Wu et al. 2015

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