{"doi":"10.1101/2022.08.04.502792","title":"Detecting Boolean Asymmetric Relationships with a Loop Counting Technique and its Implications for Analyzing Heterogeneity within Gene Expression Datasets","abstract":"Abstract Many traditional methods for analyzing gene-gene relationships focus on positive and negative correlations, both of which are a kind of ‘symmetric’ relationship. Biclustering is one such technique that typically searches for subsets of genes exhibiting correlated expression among a subset of samples. However, genes can also exhibit ‘asymmetric’ relationships, such as ‘if-then’ relationships used in boolean circuits. In this paper we develop a very general method that can be used to detect biclusters within gene-expression data that involve subsets of genes which are enriched for these ‘boolean-asymmetric’ relationships (BARs). These BAR-biclusters can correspond to heterogeneity that is driven by asymmetric gene-gene interactions, e.g., reflecting regulatory effects of one gene on another, rather than more standard symmetric interactions. Unlike typical approaches that search for BARs across the entire population, BAR-biclusters can detect asymmetric interactions that only occur among a subset of samples. We apply our method to a single-cell RNA-sequencing data-set, demonstrating that the statistically-significant BAR-biclusters indeed contain additional information not present within the more traditional ‘boolean-symmetric’-biclusters. For example, the BAR-biclusters involve different subsets of cells, and highlight different gene-pathways within the data-set. Moreover, by combining the boolean-asymmetric- and boolean-symmetric-signals, one can build linear classifiers which outperform those built using only traditional boolean-symmetric signals.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2022,"id":300333,"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":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9567,"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":11724,"name":"Wei Lin","orcid":"0000-0002-7506-3466","position":1,"is_corresponding":false},{"id":797356,"name":"Sergio R. Labra","orcid":"0000-0002-4072-1131","position":2,"is_corresponding":false},{"id":292124,"name":"Stuart A. Lipton","orcid":"0000-0002-3490-1259","position":3,"is_corresponding":false},{"id":486270,"name":"Jeremy A. Elman","orcid":"0000-0002-5840-1769","position":4,"is_corresponding":false},{"id":51360,"name":"Nicholas J. Schork","orcid":"0000-0003-0920-5013","position":5,"is_corresponding":false},{"id":990785,"name":"Aaditya V. Rangan","orcid":"0000-0003-1579-7991","position":6,"is_corresponding":false},{"id":990784,"name":"Haosheng Zhou","orcid":"0000-0001-8062-9818","position":0,"is_corresponding":true}],"reference_count":53,"raw_metadata":null,"created_at":"2026-07-19T00:31:53.559757Z","pmid":null,"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":[]}