SpatialCOC: an integrative framework for spatial continuous mapping and cross-omics correction in spatial multi-omics data
Nature Communications, vol. 17
Abstract
Integrating spatial multi-omics data presents significant challenges, particularly in uncovering the spatial patterns of cells and deciphering the real regulatory mechanisms among various omics. These insights are critical for harnessing the full potential of each modality while minimizing the impact of biotechnological biases that will lead to unstable results. Here, we introduce SpatialCOC, a framework that treats spatial information as prior knowledge to learn omics-specific spatial distributions, then discovering nonlinear correlations among modalities. The effectiveness and robustness of SpatialCOC are validated using real-world datasets, encompassing diverse tissue sections analyzed with multiple experimental techniques. Compared to existing methods, SpatialCOC excels in identifying region-specific continuous spatial domains and maintains batch-consistency across trajectory inferences. By providing a novel perspective on the interplay between spatial information and multi-omics modalities, SpatialCOC offers a flexible approach that can accommodate modality data of arbitrary dimensions.
Authors 7
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Affiliation as printed
MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
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Affiliation as printed
MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
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Affiliation as printed
School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China
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Affiliation as printed
School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China
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Affiliation as printed
MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
School of Computer Science and Technology, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
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Macau University of Science and Technology · Xi'an Jiaotong University
Affiliation as printed
Macao Institute of Systems Engineering, Macau University of Science and Technology, Macao, China
School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China
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Leiden University · First Affiliated Hospital of Xi'an Jiaotong University · Xi'an Jiaotong University
Affiliation as printed
Department of Gynecology and Obstetrics, Center for Mathematical Medical, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China. kaiye@xjtu.edu.cn
Faculty of Science, Leiden University, Leiden, The Netherlands. kaiye@xjtu.edu.cn
Genome Institute, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China. kaiye@xjtu.edu.cn
MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China. kaiye@xjtu.edu.cn
School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China. kaiye@xjtu.edu.cn
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