ABSTRACT
This chapter aims to study exposomic data. It illustrates the use of rexposome, a comprehensive Bioconductor package with functions to impute, normalize and characterize correlational structures in exposomic data, to perform exposome-wise association analysis and to test the association between exposomic and other omic variables. The study of the underlying mechanisms that link exposomic data with human health is an emerging research field with a strong potential to provide new insights into disease etiology. The rexposome project provides a freely available framework for robust, scalable, reproducible and open-source development of methods to analyze exposomic data. Multiomic data, including exposome, belonging to the INMA-Sabadell birth cohort that aims to study respiratory and neurodevelopmental disorders in children is distributed with the brgedata package. An alternative strategy of exposome-transcriptome analysis is first to reduce the dimensionality of the exposome data in a subject-wise clustering, and then regress the clustering status on the transcriptome.
