Knowledge-Guided Bayesian Factor Analysis of Multi-Omics Data in the Presence of Missingness

Music City Center 

The integration of high-dimensional multi-omics data is critical for identifying joint mechanisms underlying complex diseases and phenotypes. Bayesian factor analysis models with informative, sparsity-inducing priors based on domain knowledge can decompose these data into low-dimensional representations. However, missingness poses a challenge for inferring latent factors; traditional complete-case and imputation approaches may induce bias when partially observed modalities are not missing completely at random. We propose a novel Bayesian factor model that employs data augmentation to dynamically impute incomplete -omics layers during inference. Hierarchical priors in our model enable the incorporation of biological graphs, promoting joint selection of biologically relevant factors that may be missing. Simulation studies showed that our method was robust to ignorable missingness and outperformed the state-of-the-art in the case of block-missingness. In a real-world application to Alzheimer's disease, we achieved interpretable dimension reduction and diagnosis prediction, illustrating the ability to elucidate complex biological systems in the presence of incomplete multi-omics data.

Missing data

Bayesian factor analysis

Multi-omics integration

Dimension reduction

Multivariate data analysis

Knowledge graph 

Biometrics Section