HIP: a method for high-dimensional multi-view data integration and prediction accounting for subgroup heterogeneity.

ABSTRACT: Epidemiologic and genetic studies in many complex diseases suggest subgroup disparities (e.g. by sex, race) in disease course and patient outcomes. We consider this from the standpoint of integrative analysis where we combine information from different views (e.g. genomics, proteomics, clinical data). Existing integrative analysis methods ignore the heterogeneity in subgroups, and stacking the views and accounting for subgroup heterogeneity does not model the association among the views. We propose Heterogeneity in Integration and Prediction (HIP), a statistical approach for joint association and prediction that leverages the strengths in each view to identify molecular signatures that are shared by and specific to a subgroup. We apply HIP to proteomics and gene expression data pertaining

SUBMITTER: Butts J 

PROVIDER: S-EPMC11440091 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature