Bayesian structural equation modeling in multiple omics data with application to circadian genes.

ABSTRACT:

Motivation

It is well known that the integration among different data-sources is reliable because of its potential of unveiling new functionalities of the genomic expressions, which might be dormant in a single-source analysis. Moreover, different studies have justified the more powerful analyses of multi-platform data. Toward this, in this study, we consider the circadian genes' omics profile, such as copy number changes and RNA-sequence data along with their survival response. We develop a Bayesian structural equation modeling coupled with linear regressions and log normal accelerated failure-time regression to integrate the information between these two platforms to predict the survival of the subjects. We place conjugate priors on the regression parameters and derive the Gibbs

SUBMITTER: Maity AK 

PROVIDER: S-EPMC7332567 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature