[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81909-en":3,"doc-seo-81909-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},81909,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Causal ASCEND Scalable Two-tier Causal Discovery on High Dimensional Multi-omics Data","Biological systems follow a hierarchical, directed flow from upstream regulators to downstream effects, yet standard causal discovery and gene regulatory network (GRN) methods often ignore this tiered organization or attempt conditioning on all upstream variables, which is infeasible in high-dimensional omics. ASCEND (Ancestral Scalable Causal discovEry via iNherited Descent) uses a constraint-based, two-tier divide-and-conquer strategy with dynamically updated ancestral conditioning sets, achieving polynomial-time scaling, accurately recovering ancestral relationships, and outperforming existing GRN inference methods on simulations and real multi-omic datasets while resolving causal directionality for matched background–foreground measurements.","arXiv :2607 .04527v2 [ stat .ML] 10 Jul 2026  \nBioinformatics, 2026, pp. 1–17  \ndoi: DOI HERE  \nAdvance Access Publication Date: Day Month Year Paper  \nPAPER  \nCausal ASCEND: Scalable Two-tier Causal Discovery on High Dimensional Multi-omics Data  \nStephen Asiedu 1,∗ and David Watson 1  \n1 Department of Informatics, King’s College London, Bush House, 30 Aldwych, WC2B 4BG, London, United Kingdom  \n∗ Corresponding [authors. stephen.asiedu@kcl.ac.uk](authors. stephen.asiedu@kcl.ac.uk) , [david.watson@kcl.ac.uk](david.watson@kcl.ac.uk)  \nAbstract  \nMotivation:  \nBiological systems exhibit a hierarchical structure, characterised by directed flow from upstream regulators to downstream effects. Although this ordering provides a natural scaffold for causal inference, most causal discovery and GRN methods either ignore the tiered organisation or condition on all upstream variables, which becomes infeasible for high-dimensional omics data.  \nResults:  \nWe present ASCEND (Ancestral Scalable Causal discovEry via iNherited Descent), a constraint-based framework that leverages known two-tiered structure to enable genome-scale causal discovery. ASCEND introduces a divide-and-conquer strategy that maintains dynamically updated ancestral conditioning sets for each downstream variable, dramatically reducing the number of conditional independence tests required, and achieves polynomial-time complexity where traditional approaches face exponential blow-up. Through extensive simulations and real biological data, we demonstrate that ASCEND accurately recovers ancestral relationships, scales properly and much faster, and outperforms existing gene regulatory network inference methods in both causal precision and computational efficiency. The algorithm’s ability to resolve directionality makes it particularly suited for integrating multi-omic data where upstream regulators (e.g. , SNPs, methylation sites) and downstream responses (e.g., gene expression) are measured jointly.  \nKey words: Multi-omic integration, Causal discovery, High dimensionality  \nIntroduction  \nThe central goal of systems biology is to transition from a descriptive catalogue of molecular components to a functional map of the causal mechanisms governing life [Rebai, 2017, Lynch, 2021, Chevalley et al., 2022, Glymouret al., 2019, Ayyanathan, 2014, Hu et al., 2018] . With the maturation of high-throughput sequencing, we now possess unprecedented multi-omic profiles ranging from genomic variation to transcriptomic and proteomic responses [Manelet al., 2016, He et al., 2017, Neale and Wheeler, 2019, AbuElmagd et al., 2022] . However, the sheer dimensionality of these datasets has created a paradox of data richness with little knowledge of the true governing structure [Bates et al., 2020] . While we can observe thousands of simultaneous molecular shifts, distinguishing the primary drivers of disease from their downstream effects remains a formidable challenge.  \nBiological systems are fundamentally hierarchical. This organisation is not a mere byproduct of complexity but is rooted in the central dogma and the directional flow of information from inherited background variables to foreground variables [Danchin et al., 2007, Veenstra, 2012, Neale and Wheeler, 2019] . For example, genetic variations biologically  \nprecede transcriptomic states. In this “two-tiered” landscape, background variables act as an upstream scaffold that causally precedes foreground variables [Watson and Silva, 2022] . This inherent causal ordering provides a natural constraint that should drastically simplify the search for Gene Regulatory Networks (GRNs) .  \nExploiting this structure, however, requires paired multiomic measurements on the same samples: a stronger data requirement than the “transcriptomics-only” setting in which most GRN methods operate. Where such matched background and foreground layers are available, the tiered ordering supplies causal information that association-based methods cannot access; ye","cbCaieRpXSpJwt06","https://ap.wps.com/l/cbCaieRpXSpJwt06","pdf",900223,7,1,16,"English","en",105,"# Abstract\n# Motivation\n# Results\n# Introduction\n## Hierarchical structure in biological systems\n## Limits of existing GRN and causal discovery methods\n## Need for matched two-tier multi-omic measurements\n# ASCEND workflow and key idea","[{\"question\":\"What problem does ASCEND address in causal discovery for multi-omics data?\",\"answer\":\"ASCEND targets the inefficiency and loss of causal information that occurs when most methods either ignore hierarchical two-tier structure or condition on all upstream variables, which becomes computationally prohibitive in high-dimensional omics.\"},{\"question\":\"How does ASCEND improve scalability compared with traditional constraint-based approaches?\",\"answer\":\"ASCEND uses a divide-and-conquer framework with dynamically updated ancestral conditioning sets for each downstream variable, reducing the number of conditional independence tests and avoiding exponential blow-up, yielding polynomial-time complexity.\"},{\"question\":\"What data structure requirement enables ASCEND to recover causal directionality?\",\"answer\":\"ASCEND leverages paired multi-omic measurements where background (e.g., SNPs or methylation sites) causally precedes foreground (e.g., gene expression), allowing the two-tier ordering to provide causal constraints that association-only methods cannot use.\"}]","Causal ASCEND Scalable Two-tier Causal Discovery on High Dimensional Multi-omics Data | 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problem does ASCEND address in causal discovery for multi-omics data?","Question",{"text":77,"@type":78},"ASCEND targets the inefficiency and loss of causal information that occurs when most methods either ignore hierarchical two-tier structure or condition on all upstream variables, which becomes computationally prohibitive in high-dimensional omics.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does ASCEND improve scalability compared with traditional constraint-based approaches?",{"text":82,"@type":78},"ASCEND uses a divide-and-conquer framework with dynamically updated ancestral conditioning sets for each downstream variable, reducing the number of conditional independence tests and avoiding exponential blow-up, yielding polynomial-time complexity.",{"name":84,"@type":75,"acceptedAnswer":85},"What data structure requirement enables ASCEND to recover causal directionality?",{"text":86,"@type":78},"ASCEND leverages paired multi-omic measurements where background (e.g., SNPs or methylation sites) causally precedes foreground (e.g., gene expression), allowing the two-tier ordering to provide causal constraints that association-only methods cannot use.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & 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