Data integration between weakly linked single-cell modalities is challenging using existing methods. Therefore, we developed MaxFuse to enable matching and integration between cells from modalities such as single-cell spatial proteomic datasets and single-cell transcriptomic datasets, or other modalities where features are only weakly correlated.
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Argelaguet, R. et al. Computational principles and challenges in single-cell data integration. Nat. Biotechnol. 39, 1202–1215 (2021). A review on single-cell data integration.
Luecken, M. D. et al. Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods 19, 41–50 (2022). A review that performed benchmarking of existing single-cell integration tools.
Heumos, L. et al. Best practices for single-cell analysis across modalities. Nat. Rev. Genet. 24, 550–572 (2023). A review that described the emerging modality types for single-cell biology.
Hickey, J. W. et al. Organization of the human intestine at single-cell resolution. Nature 619, 572–584 (2023). This paper generated the human intestine tissue atlas with CODEX, snRNA-seq and snATAC-seq data.
Zhu, B. et al. Robust single-cell matching and multimodal analysis using shared and distinct features. Nat. Methods 20, 304–315 (2023). This paper presents a method that is the predecessor of MaxFuse.
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This is a summary of: Chen, S. et al. Integration of spatial and single-cell data across modalities with weakly linked features. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01935-0 (2023).
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MaxFuse enables data integration across weakly linked spatial and single-cell modalities. Nat Biotechnol (2023). https://doi.org/10.1038/s41587-023-01943-0