A hierarchical spike-and-slab model for pan-cancer survival using pan-omic data.

ABSTRACT:

Background

Pan-omics, pan-cancer analysis has advanced our understanding of the molecular heterogeneity of cancer. However, such analyses have been limited in their ability to use information from multiple sources of data (e.g., omics platforms) and multiple sample sets (e.g., cancer types) to predict clinical outcomes. We address the issue of prediction across multiple high-dimensional sources of data and sample sets by using molecular patterns identified by BIDIFAC+, a method for integrative dimension reduction of bidimensionally-linked matrices, in a Bayesian hierarchical model. Our model performs variable selection through spike-and-slab priors that borrow information across clustered data. We use this model to predict overall patient survival from the Cancer Genome Atlas with

SUBMITTER: Samorodnitsky S 

PROVIDER: S-EPMC9204947 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature