Integration of spatial and single-cell data across modalities with weakly linked features

Shuxiao Chen; Bokai Zhu; Sijia Huang; John W Hickey; Kevin Z Lin; Michael Snyder; William J Greenleaf; Garry P Nolan; Nancy R Zhang; Zongming Ma

doi:10.1038/s41587-023-01935-0

Integration of spatial and single-cell data across modalities with weakly linked features

Nat Biotechnol. 2023 Sep 7. doi: 10.1038/s41587-023-01935-0. Online ahead of print.

Authors

Shuxiao Chen^#¹, Bokai Zhu^#^{2

3}, Sijia Huang¹, John W Hickey³, Kevin Z Lin⁴, Michael Snyder⁵, William J Greenleaf⁵, Garry P Nolan^{6

7}, Nancy R Zhang⁸, Zongming Ma⁹

Affiliations

¹ Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA.
² Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA.
³ Department of Pathology, Stanford University, Stanford, CA, USA.
⁴ Department of Biostatistics, University of Washington, Seattle, WA, USA.
⁵ Department of Genetics, Stanford University, Stanford, CA, USA.
⁶ Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA. gnolan@stanford.edu.
⁷ Department of Pathology, Stanford University, Stanford, CA, USA. gnolan@stanford.edu.
⁸ Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA. nzh@wharton.upenn.edu.
⁹ Department of Statistics and Data Science, Yale University, New Haven, CT, USA. zongming.ma@yale.edu.

^# Contributed equally.

PMID: 37679544
DOI: 10.1038/s41587-023-01935-0

Abstract

Although single-cell and spatial sequencing methods enable simultaneous measurement of more than one biological modality, no technology can capture all modalities within the same cell. For current data integration methods, the feasibility of cross-modal integration relies on the existence of highly correlated, a priori 'linked' features. We describe matching X-modality via fuzzy smoothed embedding (MaxFuse), a cross-modal data integration method that, through iterative coembedding, data smoothing and cell matching, uses all information in each modality to obtain high-quality integration even when features are weakly linked. MaxFuse is modality-agnostic and demonstrates high robustness and accuracy in the weak linkage scenario, achieving 20~70% relative improvement over existing methods under key evaluation metrics on benchmarking datasets. A prototypical example of weak linkage is the integration of spatial proteomic data with single-cell sequencing data. On two example analyses of this type, MaxFuse enabled the spatial consolidation of proteomic, transcriptomic and epigenomic information at single-cell resolution on the same tissue section.

Grants and funding

U54 HG010426/HG/NHGRI NIH HHS/United States