Work

Model-based joint visualization of multiple compositional omics datasets

Year: 2020

Type: article

Abstract: The integration of multiple omics datasets measured on the same samples is a challenging task: data come from heterogeneous sources and vary in signal quality. In addition, some omics data are inherently compositional, e.g. sequence count data. Most integrative methods are limited in their ability to handle covariates, missing values, compositional structure and heteroscedasticity. In this article we introduce a flexible model-based approach to data integration to address these current limitations: COMBI. We combine concepts, such as compositional biplots and log-ratio link functions with latent variable models, and propose an attractive visualization through multiplots to improve interpretation. Using real data examples and simulations, we illustrate and compare our method with other data integration techniques. Our algorithm is available in the R-package combi.

Source: NAR Genomics and Bioinformatics

DOI: 10.1093/nargab/lqaa050

Authors: Stijn Hawinkel, Luc Bijnens, Kim-Anh Lê Cao, Olivier Thas

Institutions: Ghent University, Janssen (Belgium), Hasselt University, The University of Melbourne, University of Wollongong

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,766

Funder: Johnson and Johnson