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

• Published in: Cell reports (Cambridge) Vol. 35; no. 2; p. 108975
• Main Authors: Tyler, Scott R., Chun, Yoojin, Ribeiro, Victoria M., Grishina, Galina, Grishin, Alexander, Hoffman, Gabriel E., Do, Anh N., Bunyavanich, Supinda
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 13.04.2021; Elsevier
• Subjects: Adolescent; Adult; Allergens - administration & dosage; Allergens - immunology; Asthma - etiology; Asthma - genetics; Asthma - immunology; Asthma - microbiology; Atlases as Topic; Benchmarking; bioinformatics; Case-Control Studies; categorical clustering; Child; Child, Preschool; clinical data; Cluster Analysis; clustering; data integration; Datasets as Topic; endotypes; Epigenome; feature selection; Feces - cytology; Feces - microbiology; Female; Gene Expression Profiling; Gene Expression Regulation; Humans; Male; Microbiota - genetics; Middle Aged; multi-omics; Nasal Cavity - cytology; Nasal Cavity - microbiology; personalized medicine; Precision Medicine; Proteomics - methods; systems biology; Transcriptome; Unsupervised Machine Learning; systems biology; endotypes; categorical clustering; bioinformatics; multi-omics; clustering; data integration; feature selection; personalized medicine; clinical data
• ISSN: 2211-1247; 2211-1247
• DOI: 10.1016/j.celrep.2021.108975

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/. [Display omitted] •MANAclust enables integrated analysis of clinical and multi-omics data•Inter-variable relative information provides accurate categorical feature selection•MANAclust outperforms competing approaches for multi-omic analysis•MANAclust identifies clinically and molecularly distinct asthma clusters Clinical data commonly used in medical practice are underutilized in multi-omic analyses to identify disease endotypes. Tyler et al. present a python package called Merged Affinity Network Association Clustering (MANAclust) that automatically processes and integrates categorical and numeric data types, facilitating the inclusion of clinical data in multi-omic endotyping efforts.