High-Sensitivity Pattern Discovery in Large, Paired Multi-Omic Data Sets

• Activity Number: 384 - Next-Generation Sequencing and High-Dimensional Data
• Type: Contributed
• Date/Time: Thursday, August 12, 2021 : 12:00 PM to 1:50 PM
• Sponsor: Biometrics Section
• Author(s): Andrew Ghazi* and Kathleen Sucipto and Gholamali Rahnavard and Eric Franzosa and Lauren McIver and Jason Lloyd-Price and Emma Schwager and George Weingart and Yo Sup Moon and Xochitl Morgan and Levi Waldron and Curtis Huttenhower
• Companies: Broad Institute and Harvard TH Chan School of Public Health and Harvard TH Chan School of Public Health and Harvard TH Chan School of Public Health and Harvard TH Chan School of Public Health and Harvard TH Chan School of Public Health and Harvard TH Chan School of Public Health and Harvard TH Chan School of Public Health and Harvard TH Chan School of Public Health and University of Otago and CUNY Graduate school Public Health and Health Policy and Harvard T.H. Chan School of Public Health
• Keywords: hierarchical; association; high-dimensional; multiomics

Abstract

Modern biological screens yield enormous numbers of measurements, and finding interpretable, statistically significant associations among features is essential. Here, we present a novel hierarchical framework, HAllA (Hierarchical All-against-All association testing), for structured association discovery between paired high-dimensional datasets. HAllA efficiently integrates hierarchical nonparametric hypothesis testing with false discovery rate correction to reveal significant linear and non-linear block-wise relationships among continuous and/or categorical. We optimized and evaluated HAllA using heterogeneous synthetic datasets of known association structure, where HAllA outperformed all-against-all and other block testing approaches across a range of common similarity measures. We then applied HAllA to a series of real-world multi-omics datasets, revealing new associations between gene expression and host immune activity, the microbiome and host transcriptome, metabolomic profiling, and human health phenotypes. An open-source implementation of HAllA is freely available at http://huttenhower.sph.harvard.edu/halla along with documentation, demo datasets, and a user group.