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LIBRA: an adaptative integrative tool for paired single-cell multi-omics data

摘要There is a need for tools that integrate single-cell multi-omic data while addressing several integrative challenges simultaneously. To this end, we designed a deep-learning based tool LIBRA that performs competitively in both “integration” and “prediction” tasks based on single-cell multi-omics data. Furthermore, when assessing the predictive power across data modalities, LIBRA outperforms existing tools. LIBRA and its adaptive scheme aLIBRA, allow automatic fine-tuning 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.

作者 Xabier Martinez-de-Morentin ^[1] Sumeer A. Khan ^[2] Robert Lehmann ^[2] Sisi Qu ^[2] Alberto Maillo ^[2] Narsis A. Kiani ^[3] Felipe Prosper ^[4] Jesper Tegner ^[5] David Gomez-Cabrero ^[6] 学术成果认领

作者单位 Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona 31001, Spain ^[1] Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia ^[2] Algorithmic Dynamic Lab, Department of Oncology and Pathology, Center for Molecular Medicine, Karolinska Institute, Stockholm 17177, Sweden ^[3] Division of Hemato-Oncology, Center for Applied Medical Research CIMA, Cancer Center University of Navarra (CCUN), Navarra Institute for Health Research (IDISNA), CIBERONC, Pamplona 31008, Spain;Department of Hematology, Clinica Universidad de Navarra, CIBERONC Pamplona 31008, Spain ^[4] Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia;Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia ^[5] Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona 31001, Spain;Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia ^[6]

关键词 single-cell multi-omic Autoencoder auto-finetuning single-cell multi-omic Autoencoder auto-finetuning

栏目名称

RESEARCH ARTICLE

DOI 10.15302/J-QB-022-0318

发布时间 2024-07-26（万方平台首次上网日期，不代表论文的发表时间）

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