期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 19, 期 1, 页码 479-490出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2020.2999397
关键词
Group sparse; imaging genetics; joint non-negative matrix factorization; orthogonal subspace; schizophrenia
类别
资金
- National Institutes of Health [R01GM109068, R01MH104680, R01MH107354, RO1AR059781, R01EB006841, R01EB005846, P20GM103472]
- National Science Foundation (NSF)
- Fundamental Research Funds for the Central Universities, Chang'an University (CHD) [300102329102]
- Natural Science Foundation of Shaanxi [2019JM-536]
- China Scholarship Council [201806565009]
In this study, a novel algorithm called GJNMFO is proposed to integrate SNP, fMRI and DNA methylation data to identify risk genes, epigenetic factors, and abnormal brain regions associated with schizophrenia. The algorithm effectively discards unimportant features and improves accuracy by introducing orthogonal constraints and group sparse processing.
With the development of multi-model neuroimaging technology and gene detection technology, the efforts of integrating multi-model imaging genetics data to explore the virulence factors of schizophrenia (SZ) are still limited. To address this issue, we propose a novel algorithm called group sparse of joint non-negative matrix factorization on orthogonal subspace (GJNMFO). Our algorithm fuses single nucleotide polymorphism (SNP) data, function magnetic resonance imaging (fMRI) data and epigenetic factors (DNA methylation) by projecting three-model data into a common basis matrix and three different coefficient matrices to identify risk genes, epigenetic factors and abnormal brain regions associated with SZ. Specifically, we introduce orthogonal constraints on the basis matrix to discard unimportant features in the row of coefficient matrices. Since imaging genetics data have rich group information, we draw into group sparse on three coefficient matrices to make the extracted features more accurate. Both the simulated and real Mind Clinical Imaging Consortium (MCIC) datasets are performed to validate our approach. Simulation results show that our algorithm works better than other competing methods. Through the experiments of MCIC datasets, GJNMFO reveals a set of risk genes, epigenetic factors and abnormal brain functional regions, which have been verified to be both statistically and biologically significant.
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