Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona

Motivation: 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. Results: In this study, we present Pamona, a partial Gromov-Wasserstein distance-based manifold alignment framework that integrates heterogeneous single-cell multi-omics datasets with the aim of delineating and representing the shared and dataset-specific cellular structures across modalities. We formulate this task as a partial manifold alignment problem and develop a partial Gromov-Wasserstein optimal transport framework to solve it. Pamona identifies both shared and dataset-specific cells based on the computed probabilistic couplings of cells across datasets, and it aligns cellular modalities in a common low-dimensional space, while simultaneously preserving both shared and dataset-specific structures. Our framework can easily incorporate prior information, such as cell type annotations or cell-cell correspondence, to further improve alignment quality. We evaluated Pamona on a comprehensive set of publicly available benchmark datasets. We demonstrated that Pamona can accurately identify shared and dataset-specific cells, as well as faithfully recover and align cellular structures of heterogeneous single-cell modalities in a common space, outperforming the comparable existing methods.

Automated summary

Motivated by the need for effective approaches to integrate single-cell multi-omics data, this study presents Pamona, a partial Gromov-Wasserstein distance-based manifold alignment framework. It aims to delineate and represent the shared and dataset-specific cellular structures across modalities. Pamona demonstrates superior performance in accurately identifying shared and dataset-specific cells, recovering and aligning cellular structures, outperforming existing methods. The framework also allows for the incorporation of prior information to enhance alignment quality.