Year: 2023
Type: article
Abstract: Currently available single-cell omics technologies capture many unique features with different biological information content. Data integration aims to place cells, captured with different technologies, onto a common embedding to facilitate downstream analytical tasks. Current horizontal data integration techniques use a set of common features, thereby ignoring non-overlapping features and losing information. Here we introduce StabMap, a mosaic data integration technique that stabilizes mapping of single-cell data by exploiting the non-overlapping features. StabMap first infers a mosaic data topology based on shared features, then projects all cells onto supervised or unsupervised reference coordinates by traversing shortest paths along the topology. We show that StabMap performs well in various simulation contexts, facilitates 'multi-hop' mosaic data integration where some datasets do not share any features and enables the use of spatial gene expression features for mapping dissociated single-cell data onto a spatial transcriptomic reference.
Source: Nature Biotechnology
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $11,690
Funders: U.S. Department of Health and Human Services, Wellcome Trust, European Molecular Biology Laboratory, Cancer Research UK, Royal Society, University of Cambridge, University of Sydney, Vetenskapsrådet, Göteborgs Universitet, Barncancerfonden, Department of Education and Training, National Institutes of Health, Australian Research Council, Cancer Research UK Cambridge Institute, University of Cambridge
Awards: Statistical approaches for spatial genomics at single cell resolution, G1551767680, G1847806088, G1888620905, From Mesoderm to Blood: a Transcriptional Roadmap at Single-Cell Resolution, G3004532622, G3212486142, Comprehensive reference map construction, geolocation and data integration for HuBMAP HIVE, G55385154, G63586632, G7669185616, G797072831
OpenAlex ID: W4378212624