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IEEE/ACM Trans Comput Biol Bioinform. 2019 Feb 14. doi: 10.1109/TCBB.2019.2899568. [Epub ahead of print]

Integration of imaging (epi)genomics data for the study of schizophrenia using group sparse joint nonnegative matrix factorization.

Abstract

Schizophrenia (SZ) is a complex disease. Single nucleotide polymorphism (SNP), brain activity measured by functional magnetic resonance imaging (fMRI) and DNA methylation are all important biomarkers that can be used for the study of SZ. To our knowledge, there has been little effort to combine these three datasets together. In this study, we propose a group sparse joint nonnegative matrix factorization (GSJNMF) model to integrate SNP, fMRI and DNA methylation for the identification of multi-dimensional modules associated with SZ, which can be used to study regulatory mechanisms underlying SZ at multiple levels. The proposed GSJNMF model projects multiple types of data onto a common feature space, in which heterogeneous variables with large coefficients on the same projected bases are used to find multi-dimensional module. We also incorporate group structural information available from each dataset. The genomic factors in such modules have significant correlations or functional associations with some brain activities. We have applied the method to the analysis of data collected from the Mind Clinical Imaging Consortium (MCIC) for the study of SZ and identified significant biomarkers. These biomarkers were further used to identify genes and corresponding brain regions, which were confirmed to be significantly associated with SZ.

PMID:
30762565
DOI:
10.1109/TCBB.2019.2899568

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