{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T17:12:40Z","timestamp":1777396360098,"version":"3.51.4"},"reference-count":35,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T00:00:00Z","timestamp":1697500800000},"content-version":"vor","delay-in-days":46,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Quant. Biol."],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:sec><jats:label\/><jats:p>There is a need for tools that integrate single\u2010cell multi\u2010omic data while addressing several integrative challenges simultaneously. To this end, we designed a deep\u2010learning based tool LIBRA that performs competitively in both \u201cintegration\u201d and \u201cprediction\u201d tasks based on single\u2010cell multi\u2010omics data. Furthermore, when assessing the predictive power across data modalities, LIBRA outperforms existing tools. LIBRA and its adaptive scheme aLIBRA, allow automatic fine\u2010tuning for users with limited effort. Additionally, aLIBRA allows experienced users to implement custom configurations. The LIBRA toolbox is freely available as R and Python libraries.<\/jats:p><\/jats:sec><jats:sec><jats:title>Background<\/jats:title><jats:p>Single\u2010cell multi\u2010omics technologies allow a profound system\u2010level biology understanding of cells and tissues. However, an integrative and possibly systems\u2010based analysis capturing the different modalities is challenging. In response, bioinformatics and machine learning methodologies are being developed for multi\u2010omics single\u2010cell analysis. It is unclear whether current tools can address the dual aspect of modality integration and prediction across modalities without requiring extensive parameter fine\u2010tuning.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We designed LIBRA, a neural network based framework, to learn translation between paired multi\u2010omics profiles so that a shared latent space is constructed. Additionally, we implemented a variation, aLIBRA, that allows automatic fine\u2010tuning by identifying parameter combinations that optimize both the integrative and predictive tasks. All model parameters and evaluation metrics are made available to users with minimal user iteration. Furthermore, aLIBRA allows experienced users to implement custom configurations. The LIBRA toolbox is freely available as R and Python libraries at GitHub (TranslationalBioinformaticsUnit\/LIBRA).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>LIBRA was evaluated in eight multi\u2010omic single\u2010cell data\u2010sets, including three combinations of omics. We observed that LIBRA is a state\u2010of\u2010the\u2010art tool when evaluating the ability to increase cell\u2010type (clustering) resolution in the integrated latent space. Furthermore, when assessing the predictive power across data modalities, such as predictive chromatin accessibility from gene expression, LIBRA outperforms existing tools. As expected, adaptive parameter optimization (aLIBRA) significantly boosted the performance of learning predictive models from paired data\u2010sets.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>LIBRA is a versatile tool that performs competitively in both \u201cintegration\u201d and \u201cprediction\u201d tasks based on single\u2010cell multi\u2010omics data. LIBRA is a data\u2010driven robust platform that includes an adaptive learning scheme.<\/jats:p><\/jats:sec>","DOI":"10.15302\/j-qb-022-0318","type":"journal-article","created":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T01:00:52Z","timestamp":1676941252000},"page":"246-259","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["LIBRA: an adaptative integrative tool for paired single\u2010cell multi\u2010omics data"],"prefix":"10.1002","volume":"11","author":[{"given":"Xabier","family":"Martinez\u2010de\u2010Morentin","sequence":"first","affiliation":[{"name":"Navarrabiomed Complejo Hospitalario de Navarra (CHN) Universidad P\u00fablica de Navarra (UPNA) IdiSNA  Pamplona 31001 Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sumeer A.","family":"Khan","sequence":"additional","affiliation":[{"name":"Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and Technology  Thuwal 23955 Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Lehmann","sequence":"additional","affiliation":[{"name":"Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and Technology  Thuwal 23955 Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sisi","family":"Qu","sequence":"additional","affiliation":[{"name":"Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and Technology  Thuwal 23955 Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alberto","family":"Maillo","sequence":"additional","affiliation":[{"name":"Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and Technology  Thuwal 23955 Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Narsis A.","family":"Kiani","sequence":"additional","affiliation":[{"name":"Algorithmic Dynamic Lab Department of Oncology and Pathology Center for Molecular Medicine Karolinska Institute  Stockholm 17177 Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Felipe","family":"Prosper","sequence":"additional","affiliation":[{"name":"Division of Hemato\u2010Oncology Center for Applied Medical Research CIMA Cancer Center University of Navarra (CCUN) Navarra Institute for Health Research (IDISNA) CIBERONC  Pamplona 31008 Spain"},{"name":"Department of Hematology Clinica Universidad de Navarra CIBERONC  Pamplona 31008 Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jesper","family":"Tegner","sequence":"additional","affiliation":[{"name":"Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and Technology  Thuwal 23955 Saudi Arabia"},{"name":"Computer, Electrical and Mathematical Sciences and Engineering Division 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