Multiblock discriminant analysis for integrative genomic study

A roadmap for multi-omics data integration using deep learning

High-throughput next-generation sequencing now makes it possible to generate a vast amount of multi-omics data for various applications. These data have revolutionized biomedical research by providing a more comprehensive understanding of the biological systems and molecular mechanisms of disease development. Recently, deep learning (DL) algorithms have become one of the most promising methods in multi-omics data analysis, due to their predictive performance and capability of capturing nonlinear and hierarchical features. While integrating and translating multi-omics data into useful functional insights remain the biggest bottleneck, there is a clear trend towards incorporating multi-omics analysis in biomedical research to help explain the complex relationships between molecular layers. Multi-omics data have a role to improve prevention, early detection and prediction; monitor progression; interpret patterns and endotyping; and design personalized treatments. In this review, we outline a roadmap of multi-omics integration using DL and offer a practical perspective into the advantages, challenges and barriers to the implementation of DL in multi-omics data.

Multi-block Analysis of Genomic Data Using Generalized Canonical Correlation Analysis

Recently, there have been many studies in medicine related to genetic analysis. Many genetic studies have been performed to find genes associated with complex diseases. To find out how genes are related to disease, we need to understand not only the simple relationship of genotypes but also the way they are related to phenotype. Multi-block data, which is a summation form of variable sets, is used for enhancing the analysis of the relationships of different blocks. By identifying relationships through a multi-block data form, we can understand the association between the blocks in comprehending the correlation between them. Several statistical analysis methods have been developed to understand the relationship between multi-block data. In this paper, we will use generalized canonical correlation methodology to analyze multi-block data from the Korean Association Resource project, which has a combination of single nucleotide polymorphism blocks, phenotype blocks, and disease blocks.

Multi-Block Bipartite Graph for Integrative Genomic Analysis

Human diseases involve a sequence of complex interactions between multiple biological processes. In particular, multiple genomic data such as Single Nucleotide Polymorphism (SNP), Copy Number Variation (CNV), DNA Methylation (DM), and their interactions simultaneously play an important role in human diseases. However, despite 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 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 multiple genomic data but also incorporate 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 performance was assessed by simulation experiments that implement practical situations. We also applied the method to the human brain data of psychiatric disorders. The experimental results were analyzed by maximum edge biclique and biclustering, and biological findings were discussed.

Integration of multi-omics data for integrative gene regulatory network inference

Gene regulatory networks provide comprehensive insights and indepth understanding of complex biological processes. The molecular interactions of gene regulatory networks are inferred from a single type of genomic data, e.g., gene expression data in most research. However, gene expression is a product of sequential interactions of multiple biological processes, such as DNA sequence variations, copy number variations, histone modifications, transcription factors, and DNA methylations. The recent rapid advances of high-throughput omics technologies enable one to measure multiple types of omics data, called 'multi-omics data', that represent the various biological processes. In this paper, we propose an Integrative Gene Regulatory Network inference method (iGRN) that incorporates multi-omics data and their interactions in gene regulatory networks. In addition to gene expressions, copy number variations and DNA methylations were considered for multi-omics data in this paper. The intensive experiments were carried out with simulation data, where iGRN's capability that infers the integrative gene regulatory network is assessed. Through the experiments, iGRN shows its better performance on model representation and interpretation than other integrative methods in gene regulatory network inference. iGRN was also applied to a human brain dataset of psychiatric disorders, and the biological network of psychiatric disorders was analysed.

Genomic analyses of African Trypanozoon strains to assess evolutionary relationships and identify markers for strain identification

African trypanosomes of the sub-genus Trypanozoon) are eukaryotic parasitesthat cause disease in either humans or livestock. The development of genomic resources can be of great use to those interested in studying and controlling the spread of these trypanosomes. Here we present a large comparative analysis of Trypanozoon whole genomes, 83 in total, including human and animal infective African trypanosomes: 21 T. brucei brucei, 22 T. b. gambiense, 35 T. b. rhodesiense and 4 T. evansi strains, of which 21 were from Uganda. We constructed a maximum likelihood phylogeny based on 162,210 single nucleotide polymorphisms (SNPs.) The three Trypanosoma brucei sub-species and Trypanosoma evansi are not monophyletic, confirming earlier studies that indicated high similarity among Trypanosoma “sub-species”. We also used discriminant analysis of principal components (DAPC) on the same set of SNPs, identifying seven genetic clusters. These clusters do not correspond well with existing taxonomic classifications, in agreement with the phylogenetic analysis. Geographic origin is reflected in both the phylogeny and clustering analysis. Finally, we used sparse linear discriminant analysis to rank SNPs by their informativeness in differentiating the strains in our data set. As few as 84 SNPs can completely distinguish the strains used in our study, and discriminant analysis was still able to detect genetic structure using as few as 10 SNPs. Our results reinforce earlier results of high genetic similarity between the African Trypanozoon. Despite this, a small subset of SNPs can be used to identify genetic markers that can be used for strain identification or other epidemiological investigations.

Trypanosomes are a major health threat to the people and livestock of Sub-Saharan Africa. Building genomic resources and understanding the genetic structure of these parasites will aid researchers trying to control their spread. To this end, we compared the genomes from 83 trypanosome strains, identifying 162,210 single nucleotide polymorphisms (SNPs) between them. Our analysis shows high genetic similarity between the trypanosomes, and confirms earlier results indicating that the traditional taxonomic classifications do not correspond well with genetic data. Further, we demonstrate that, despite the high genetic similarity, each strain in the study can be distinguished using as few as 84 SNPs, suggesting that a small number of SNPs can be useful for tracking and classifying populations of African trypanosomes.