Statistical Integration of Heterogeneous Data with PO2PLS

Abstract

The availability of multi-omics data has revolutionized the life sciences by creating avenues for integrated system-level approaches. Data integration links the information across datasets to better understand the underlying biological processes. However, high-dimensionality, correlations and heterogeneity pose statistical and computational challenges. We propose a general framework, probabilistic two-way partial least squares (PO2PLS), which addresses these challenges. PO2PLS models the relationship between two datasets using joint and data-specific latent variables. For maximum likelihood estimation of the parameters, we implement a fast EM algorithm and show that the estimator is asymptotically normally distributed. A global test for testing the relationship between two datasets is proposed, and its asymptotic distribution is derived. Notably, several existing omics integration methods are special cases of PO2PLS. Via extensive simulations, we show that PO2PLS performs better than alternatives in feature selection and prediction performance. In addition, the asymptotic distribution appears to hold when the sample size is sufficiently large. We illustrate PO2PLS with two examples from commonly used study designs: a large population cohort and a small case-control study. Besides recovering known relationships, PO2PLS also identified novel findings. The methods are implemented in our R-package PO2PLS. Supplementary materials for this article are available online.

Statistical integration of multi-omics and drug screening data from cell lines

“…For clinical and epidemiological studies, the multi-omics integration approaches, algorithmic and probabilistic two-way orthogonal partial least squares (O2PLS [8] resp. PO2PLS [9]), model the systematic difference between datasets. For small datasets, algorithmic approaches tend to overfit and a probabilistic approach is preferred [7].…”