Integrative Analysis of Multi-Omics Data Based on Blockwise Sparse Principal Components.

ABSTRACT: The recent development of high-throughput technology has allowed us to accumulate vast amounts of multi-omics data. Because even single omics data have a large number of variables, integrated analysis of multi-omics data suffers from problems such as computational instability and variable redundancy. Most multi-omics data analyses apply single supervised analysis, repeatedly, for dimensional reduction and variable selection. However, these approaches cannot avoid the problems of redundancy and collinearity of variables. In this study, we propose a novel approach using blockwise component analysis. This would solve the limitations of current methods by applying variable clustering and sparse principal component (sPC) analysis. Our approach consists of two stages. The first stage identifies

SUBMITTER: Park M 

PROVIDER: S-EPMC7663540 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature