Motivation: High-dimensional omic data derived from different technological platforms have been extensively used to facilitate comprehensive understanding of disease mechanisms and to determine personalized health treatments. Numerous studies have integrated multi-platform omic data; however, few have efficiently and simultaneously addressed the problems that arise from high dimensionality and complex correlations.

Results: We propose a statistical framework of shared informative factor models that can jointly analyze multi-platform omic data and explore their associations with a disease phenotype. The common disease-associated sample characteristics across different data types can be captured through the shared structure space, while the corresponding weights of genetic variables directly index the strengths of their association with the phenotype. Extensive simulation studies demonstrate the performance of the proposed method in terms of biomarker detection accuracy via comparisons with three popular regularized regression methods. We also apply the proposed method to The Cancer Genome Atlas lung adenocarcinoma dataset to jointly explore associations of mRNA expression and protein expression with smoking status. Many of the identified biomarkers belong to key pathways for lung tumorigenesis, some of which are known to show differential expression across smoking levels. We discover potential biomarkers that reveal different mechanisms of lung tumorigenesis between light smokers and heavy smokers.

Availability and Implementation: R code to implement the new method can be downloaded from

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