Print Email Facebook Twitter Statistical integration of heterogeneous omics data Title Statistical integration of heterogeneous omics data: Probabilistic two-way partial least squares (PO2PLS) Author el Bouhaddani, Said (University Medical Center Utrecht) Uh, Hae Won (University Medical Center Utrecht) Jongbloed, G. (TU Delft Statistics) Houwing-Duistermaat, Jeanine (University Medical Center Utrecht; University of Leeds; University of Bologna) Date 2022 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), that 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 propose a novel fast EM algorithm and show that the estimator is asymptotically normally distributed. A global test for the relationship between two datasets is proposed, specifically addressing the high dimensionality, and its asymptotic distribution is derived. Notably, several existing data 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. Subject EM algorithmglobal testheterogeneityidentifiabilitylatent variable modelsprobabilistic O2PLS To reference this document use: http://resolver.tudelft.nl/uuid:3ab62a90-1ff9-4341-b503-c488b2e425ab DOI https://doi.org/10.1111/rssc.12583 ISSN 0035-9254 Source Journal of the Royal Statistical Society. Series C: Applied Statistics, 71 (5), 1451-1470 Part of collection Institutional Repository Document type journal article Rights © 2022 Said el Bouhaddani, Hae Won Uh, G. Jongbloed, Jeanine Houwing-Duistermaat Files PDF Royal_Stata_Society_Serie ... listic.pdf 1.3 MB Close viewer /islandora/object/uuid:3ab62a90-1ff9-4341-b503-c488b2e425ab/datastream/OBJ/view