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

Abstract

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.

Radiovesicolomics-new approach in medical imaging

“…Such analyses, combined with results of high-throughput techniques, produce a vast number of multiparametric (quantitative and qualitative) data from less number of examined samples. It needs systematic and multimodal analyses for integration of omics datasets and selection highly correlated biological features using different bioinformatics methods like Canonical Correlation Analysis (CCA) ( Jun et al, 2018 ; Turek et al, 2020 ; Wróbel 2021 ) or deep/machine learning algorithms for better selection of biological interrelationships ( Stahlschmidt et al, 2022 ).…”