{"doi":"10.1093/bioinformatics/btac232","title":"High-sensitivity pattern discovery in large, paired multiomic datasets","abstract":"MOTIVATION: Modern biological screens yield enormous numbers of measurements, and identifying and interpreting statistically significant associations among features are essential. In experiments featuring multiple high-dimensional datasets collected from the same set of samples, it is useful to identify groups of associated features between the datasets in a way that provides high statistical power and false discovery rate (FDR) control. RESULTS: 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 hypothesis testing with FDR correction to reveal significant linear and non-linear block-wise relationships among continuous and/or categorical data. 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 multiomics datasets, revealing new associations between gene expression and host immune activity, the microbiome and host transcriptome, metabolomic profiling and human health phenotypes. AVAILABILITY AND IMPLEMENTATION: 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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.","journal":"Bioinformatics","year":2022,"id":235233,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":83,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9504,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":809666,"name":"Kathleen Sucipto","orcid":null,"position":1,"is_corresponding":false},{"id":537557,"name":"Ali Rahnavard","orcid":"0000-0002-9710-0248","position":2,"is_corresponding":false},{"id":225246,"name":"Eric A. Franzosa","orcid":"0000-0002-8798-7068","position":3,"is_corresponding":false},{"id":550198,"name":"Lauren J. McIver","orcid":"0000-0002-2199-4310","position":4,"is_corresponding":false},{"id":225247,"name":"Jason Lloyd‐Price","orcid":"0000-0002-0112-190X","position":5,"is_corresponding":false},{"id":614246,"name":"Emma Schwager","orcid":"0000-0002-7385-8994","position":6,"is_corresponding":false},{"id":551197,"name":"George Weingart","orcid":null,"position":7,"is_corresponding":false},{"id":808781,"name":"Yo Sup Moon","orcid":"0000-0003-3550-4607","position":8,"is_corresponding":false},{"id":809126,"name":"Xochitl C. Morgan","orcid":"0000-0002-6264-6961","position":9,"is_corresponding":false},{"id":489,"name":"Levi Waldron","orcid":"0000-0003-2725-0694","position":10,"is_corresponding":false},{"id":493,"name":"Curtis Huttenhower","orcid":"0000-0002-1110-0096","position":11,"is_corresponding":false},{"id":531944,"name":"Andrew R. Ghazi","orcid":"0000-0002-8888-946X","position":0,"is_corresponding":true}],"reference_count":68,"raw_metadata":null,"created_at":"2026-07-19T00:21:48.116752Z","pmid":"35758795","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}