Group Sparse Joint Non-Negative Matrix Factorization on Orthogonal Subspace for Multi-Modal Imaging Genetics Data Analysis.

ABSTRACT: 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 unim

SUBMITTER: Peng P 

PROVIDER: S-EPMC7758677 | biostudies-literature | 2022 Jan-Feb

REPOSITORIES: biostudies-literature