Description: * Hierarchical All-against-All association testing (HAllA) is computational method to find multi-resolution associations in high-dimensional, heterogeneous datasets.
- HAllA is an end-to-end statistical method for Hierarchical All-against-All discovery of significant relationships among data features with high power. HAllA is robust to data type, operating both on continuous and categorical values, and works well both on homogeneous datasets (where all measurements are of the same type, e.g. gene expression microarrays) and on heterogeneous data (containing measurements with different units or types, e.g. patient clinical metadata). Finally, it is also aware of multiple input, multiple output problems, in which data might contain of two (or more) distinct subsets sharing an index (e.g. clinical metadata, genotypes, microarrays, and microbiomes all drawn from the same subjects). In all of these cases, HAllA will identify which pairs of features (genes, microbes, loci, etc.) share statistically significant information, without getting tripped up by high-dimensionality.
Gholamali Rahnavard, Eric A. Franzosa, Lauren J. McIver, Emma Schwager, Jason Lloyd-Price, George Weingart, Yo Sup Moon, Xochitl C. Morgan, Levi Waldron, Curtis Huttenhower, “High-sensitivity pattern discovery in large multi’omic datasets” .