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

作　　者：Xabier Martinez-de-Morentin Sumeer AKhan Robert Lehmann Sisi Qu Alberto Maillo Narsis AKiani Felipe Prosper Jesper Tegner David Gomez-Cabrero 

机构地区：[1]Navarrabiomed,Complejo Hospitalario de Navarra(CHN),Universidad Pública de Navarra(UPNA),IdiSNA,Pamplona 31001,Spain [2]Biological and Environmental Sciences and Engineering Division,King Abdullah University of Science and Technology,Thuwal 23955,Saudi Arabia [3]Algorithmic Dynamic Lab,Department of Oncology and Pathology,Center for Molecular Medicine,Karolinska Institute,Stockholm 17177,Sweden [4]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 [5]Department of Hematology,Clinica Universidad de Navarra,CIBERONC Pamplona 31008,Spain [6]Computer,Electrical and Mathematical Sciences and Engineering Division,King Abdullah University of Science and Technology,Thuwal 23955,Saudi Arabia

出　　处：《Quantitative Biology》2023年第3期246-259,共14页定量生物学（英文版）

基　　金：supported by grants from the European Union under the Horizon 2020 programme(MultipleMS grant agreement 733161)to NK;from the Spanish Government,through project PID2019-111192GA-I00(MICINN)to DGC.

摘　　要：Background:Single-cell multi-omics technologies allow a profound system-level biology understanding of cells and tissues.However,an integrative and possibly systems-based analysis capturing the different modalities is challenging.In response,bioinformatics and machine learning methodologies are being developed for multi-omics single-cell analysis.It is unclear whether current tools can address the dual aspect of modality integration and prediction across modalities without requiring extensive parameter fine-tuning.Methods:We designed LIBRA,a neural network based framework,to learn translation between paired multi-omics profiles so that a shared latent space is constructed.Additionally,we implemented a variation,aLIBRA,that allows automatic fine-tuning 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).Results:LIBRA was evaluated in eight multi-omic single-cell data-sets,including three combinations of omics.We observed that LIBRA is a state-of-the-art tool when evaluating the ability to increase cell-type(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-sets.Conclusion:LIBRA is a versatile tool that performs competitively in both“integration”and“prediction”tasks based on single-cell multi-omics data.LIBRA is a data-driven robust platform that includes an adaptive learning scheme.

关 键 词：SINGLE-CELL multi-omic Autoencoder auto-finetuning 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]