High-sensitivity pattern discovery in large, paired multi-omic datasets with HAllA

Modern biological screens yield enormous numbers of measurements and finding statistically significant associations among features with ease of interpretation is essential. Here, we present a novel hierarchical framework, HAllA (Hierarchical All-against-All association testing), for well-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 (Python) implementation of HAllA is freely available at http://huttenhower.sph.harvard.edu/halla along with documentation, demo datasets, and a user group.