{"doi":"10.1093/bioadv/vbac044","title":"<i>fast.adonis</i>: a computationally efficient non-parametric multivariate analysis of microbiome data for large-scale studies","abstract":"Abstract Motivation Nonparametric multivariate analysis has been widely used to identify variables associated with a dissimilarity matrix and to quantify their contribution. For very large studies (n≥5000) and many explanatory variables, existing software packages (e.g. adonis and adonis2 in vegan) are computationally intensive when conducting sequential multivariate analysis with permutations or bootstrapping. Moreover, for subjects from a complex sampling design, we need to adjust for sampling weights to derive an unbiased estimate. Results We implemented an R function fast.adonis to overcome these computational challenges in large-scale studies. fast.adonis generates results consistent with adonis/adonis2 but much faster. For complex sampling studies, fast.adonis integrates sampling weights algebraically to mimic the source population; thus, analysis can be completed very fast without requiring a large amount of memory. Availability and implementation fast.adonis is implemented using R and is publicly available at https://github.com/jennylsl/fast.adonis. Supplementary information Supplementary data are available at Bioinformatics Advances online.","journal":"Bioinformatics Advances","year":2022,"id":245280,"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":23,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9499,"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":95612,"name":"Emily Vogtmann","orcid":"0000-0003-1355-5593","position":1,"is_corresponding":false},{"id":28634,"name":"Barry I. Graubard","orcid":"0000-0002-6787-1105","position":2,"is_corresponding":false},{"id":265760,"name":"Mitchell H. Gail","orcid":"0000-0002-3919-3263","position":3,"is_corresponding":false},{"id":217938,"name":"Christian C. Abnet","orcid":"0000-0002-3008-7843","position":4,"is_corresponding":false},{"id":5285,"name":"Jianxin Shi","orcid":"0000-0001-8606-4707","position":5,"is_corresponding":false},{"id":852573,"name":"Shilan Li","orcid":"0000-0002-1132-1550","position":0,"is_corresponding":true}],"reference_count":10,"raw_metadata":null,"created_at":"2026-07-19T00:23:34.974895Z","pmid":"36704711","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":[]}