Integration Analysis of Three Omics Data Using Penalized Regression Methods: An Application to Bladder Cancer.

ABSTRACT: Omics data integration is becoming necessary to investigate the genomic mechanisms involved in complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological processes. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we propose a permutation-based method to concomitantly assess significance and correct by multiple testing with the MaxT algorithm. This was applied with penalized regression methods (LASSO and ENET) when exploring relationships between common genetic variants, DNA methylation and gene

SUBMITTER: Pineda S 

PROVIDER: S-EPMC4672920 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature