PT  - JOURNAL ARTICLE
AU  - Ricard Argelaguet
AU  - Britta Velten
AU  - Damien Arnol
AU  - Sascha Dietrich
AU  - Thorsten Zenz
AU  - John C. Marioni
AU  - Florian Buettner
AU  - Wolfgang Huber
AU  - Oliver Stegle
TI  - Multi-Omics factor analysis disentangles heterogeneity in blood cancer
AID  - 10.1101/217554
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 217554
4099  - http://biorxiv.org/content/early/2017/11/10/217554.short
4100  - http://biorxiv.org/content/early/2017/11/10/217554.full
AB  - Multi-omic studies in large cohorts promise to characterize biological processes across molecular layers including genome, transcriptome, epigenome, proteome and perturbation phenotypes. However, methods for integrating multi-omic datasets in an unsupervised manner are lacking. We present Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in a multi-omics dataset. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability across data modalities, thereby enabling a variety of downstream analyses, including factor annotation, data imputation and the detection of outlier samples. We applied MOFA to a study of 200 patient samples of chronic lymphocytic leukemia (CLL) profiled for somatic mutations, RNA expression, DNA methylation and ex-vivo responses to a panel of 63 drugs. MOFA discovered known dimensions of disease heterogeneity, including immunoglobulin heavy chain variable region (IGHV) status and trisomy of chromosome 12, as well as previously underappreciated drivers of variation, such as response to oxidative stress. These learnt factors capture key dimensions of inter-patient heterogeneity and enhance prediction accuracy of clinical outcomes.