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

Quantitative Biology 2023年第3期11卷 246-259页

作者： Xabier Martinez-de-Morentin Sumeer AKhan Robert Lehmann Sisi Qu Alberto Maillo Narsis AKiani Felipe Prosper Jesper Tegner David Gomez-Cabrero Navarrabiomed Complejo Hospitalario de Navarra(CHN)Universidad Pública de Navarra(UPNA)IdiSNAPamplona 31001Spain Biological and Environmental Sciences and Engineering Division King Abdullah University of Science and TechnologyThuwal 23955Saudi Arabia Algorithmic Dynamic Lab Department of Oncology and PathologyCenter for Molecular MedicineKarolinska InstituteStockholm 17177Sweden Division of Hemato-Oncology Center for Applied Medical Research CIMACancer Center University of Navarra(CCUN)Navarra Institute for Health Research(IDISNA)CIBERONCPamplona 31008Spain Department of Hematology Clinica Universidad de NavarraCIBERONC Pamplona 31008Spain Computer Electrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal 23955Saudi Arabia

Background:Single-cell multi-omics technologies allow a profound system-level biology understanding of cells and ***,an integrative and possibly systems-based analysis capturing the different modalities is *** response,bioinformatics and machine learning methodologies are being developed for multi-omics single-cell *** is unclear whether current tools can address the dual aspect of modality integration and prediction across modalities without requiring extensive parameter ***:We designed LIBRA,a neural network based framework,to learn translation between paired multi-omics profiles so that a shared latent space is ***,we implemented a variation,aLIBRA,that allows automatic fine-tuning by identifying parameter combinations that optimize both the integrative and predictive *** model parameters and evaluation metrics are made available to users with minimal user ***,aLIBRA allows experienced users to implement custom *** LIBRA toolbox is freely available as R and Python libraries at GitHub(TranslationalBioinformaticsUnit/LIBRA).Results:LIBRA was evaluated in eight multi-omic single-cell data-sets,including three combinations of *** observed that LIBRA is a state-of-the-art tool when evaluating the ability to increase cell-type(clustering)resolution in the integrated latent ***,when assessing the predictive power across data modalities,such as predictive chromatin accessibility from gene expression,LIBRA outperforms existing *** expected,adaptive parameter optimization(aLIBRA)significantly boosted the performance of learning predictive models from paired ***:LIBRA is a versatile tool that performs competitively in both“integration”and“prediction”tasks based on single-cell multi-omics *** is a data-driven robust platform that includes an adaptive learning scheme.

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

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