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Single-cell multi-omics sequencing data can provide a comprehensive molecular view of cells. However, effective approaches for the integrative analysis of such data are challenging. Existing manifold alignment methods demonstrated the state-of-the-art performance on single-cell multi-omics data integration, but they are often limited by requiring that single-cell datasets be derived from the same underlying cellular structure. In this study, we present Pamona, a partial Gromov-Wasserstein distance based manifold alignment framework that
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