Abstract
Although clinical and laboratory data have long been used to guide medical practice, this information is rarely integrated with multi-omic data to identify endotypes. We present Merged Affinity Network Association Clustering (MANAclust), a coding-free, automated pipeline enabling integration of categorical and numeric data spanning clinical and multi-omic profiles for unsupervised clustering to identify disease subsets. Using simulations and real-world data from The Cancer Genome Atlas, we demonstrate that MANAclust's feature selection algorithms are accurate and outperform competitors. We also apply MANAclust to a clinically and multi-omically phenotyped asthma cohort. MANAclust identifies clinically and molecularly distinct clusters, including heterogeneous groups of "healthy controls" and viral and allergy-driven subsets of asthmatic subjects. We also find that subjects with similar clinical presentations have disparate molecular profiles, highlighting the need for additional testing to uncover asthma endotypes. This work facilitates data-driven personalized medicine through integration of clinical parameters with multi-omics. MANAclust is freely available at https://bitbucket.org/scottyler892/manaclust/src/master/.