Merged Affinity Network Association Clustering: Joint multi-omic/clinical clustering to identify disease endotypes.

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" a

SUBMITTER: Tyler SR 

PROVIDER: S-EPMC8195153 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature