TiMEG: an integrative statistical method for partially missing multi-omics data.

ABSTRACT: Multi-omics data integration is widely used to understand the genetic architecture of disease. In multi-omics association analysis, data collected on multiple omics for the same set of individuals are immensely important for biomarker identification. But when the sample size of such data is limited, the presence of partially missing individual-level observations poses a major challenge in data integration. More often, genotype data are available for all individuals under study but gene expression and/or methylation information are missing for different subsets of those individuals. Here, we develop a statistical model TiMEG, for the identification of disease-associated biomarkers in a case-control paradigm by integrating the above-mentioned data types, especially, in presence of missing om

SUBMITTER: Das S 

PROVIDER: S-EPMC8674330 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature