An integrative genomic study for multimodal genomic data using multi-block bipartite graph

摘要

Human diseases involve a sequence of complex interactions in multiple biological processes. In particular, multiple genomic data such as Single Nucleotide Polymorphism (SNP), Copy Number Variation (CNV), and DNA Methylation (DM) and their interactions simultaneously play an important role in the variation of mRNA transcription in human diseases. However, despite of the widely known complex multi-layer biological processes and increased availability of the heterogeneous genomic data, most research has considered only a single type of the genomic data. Furthermore, recent integrative genomic studies for the multiple genomic data have also been facing difficulties due to the high-dimensionality and complexity, especially when considering their intra- and inter-block interactions. In this paper, we introduce a novel multi-block bipartite graph and its inference methods, MB2I and sMB2I, for the integrative genomic study. The proposed methods not only integrate the multiple genomic data but also incorporate their intra/inter-block interactions by using a multi-block bipartite graph. In addition, the methods can be used to predict quantitative traits (e.g. gene expression, survival time) from the multi-block genomic data. The outstanding performance was assessed by simulation experiments that implement practical situations.