{"doi":"10.1093/bioinformatics/btab290","title":"‘Single-subject studies’-derived analyses unveil altered biomechanisms between very small cohorts: implications for rare diseases","abstract":"MOTIVATION: Identifying altered transcripts between very small human cohorts is particularly challenging and is compounded by the low accrual rate of human subjects in rare diseases or sub-stratified common disorders. Yet, single-subject studies (S3) can compare paired transcriptome samples drawn from the same patient under two conditions (e.g. treated versus pre-treatment) and suggest patient-specific responsive biomechanisms based on the overrepresentation of functionally defined gene sets. These improve statistical power by: (i) reducing the total features tested and (ii) relaxing the requirement of within-cohort uniformity at the transcript level. We propose Inter-N-of-1, a novel method, to identify meaningful differences between very small cohorts by using the effect size of 'single-subject-study'-derived responsive biological mechanisms. RESULTS: In each subject, Inter-N-of-1 requires applying previously published S3-type N-of-1-pathways MixEnrich to two paired samples (e.g. diseased versus unaffected tissues) for determining patient-specific enriched genes sets: Odds Ratios (S3-OR) and S3-variance using Gene Ontology Biological Processes. To evaluate small cohorts, we calculated the precision and recall of Inter-N-of-1 and that of a control method (GLM+EGS) when comparing two cohorts of decreasing sizes (from 20 versus 20 to 2 versus 2) in a comprehensive six-parameter simulation and in a proof-of-concept clinical dataset. In simulations, the Inter-N-of-1 median precision and recall are > 90% and >75% in cohorts of 3 versus 3 distinct subjects (regardless of the parameter values), whereas conventional methods outperform Inter-N-of-1 at sample sizes 9 versus 9 and larger. Similar results were obtained in the clinical proof-of-concept dataset. AVAILABILITY AND IMPLEMENTATION: R software is available at Lussierlab.net/BSSD.","journal":"Bioinformatics","year":2021,"id":202594,"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":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9285,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":782702,"name":"Nima Pouladi","orcid":"0009-0002-2151-7052","position":1,"is_corresponding":false},{"id":513867,"name":"Samir Rachid Zaim","orcid":"0009-0007-9256-2324","position":2,"is_corresponding":false},{"id":514389,"name":"Colleen Kenost","orcid":null,"position":3,"is_corresponding":false},{"id":63152,"name":"Joanne Berghout","orcid":"0000-0002-9250-9159","position":4,"is_corresponding":false},{"id":782703,"name":"Walter W. Piegorsch","orcid":"0000-0003-2725-5604","position":5,"is_corresponding":false},{"id":15428,"name":"Yves A. Lussier","orcid":"0000-0001-9854-1005","position":6,"is_corresponding":false},{"id":783245,"name":"Dillon Aberasturi","orcid":null,"position":0,"is_corresponding":true}],"reference_count":55,"raw_metadata":null,"created_at":"2026-07-18T23:51:10.291262Z","pmid":"34252934","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":[]}