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SpSeudoMap: cell type mapping of spatial transcriptomics using unmatched single-cell RNA-seq data.

ABSTRACT: Since many single-cell RNA-seq (scRNA-seq) data are obtained after cell sorting, such as when investigating immune cells, tracking cellular landscape by integrating single-cell data with spatial transcriptomic data is limited due to cell type and cell composition mismatch between the two datasets. We developed a method, spSeudoMap, which utilizes sorted scRNA-seq data to create virtual cell mixtures that closely mimic the gene expression of spatial data and trains a domain adaptation model for predicting spatial cell compositions. The method was applied in brain and breast cancer tissues and accurately predicted the topography of cell subpopulations. spSeudoMap may help clarify the roles of a few, but crucial cell types.

SUBMITTER: Bae S

PROVIDER: S-EPMC10021938 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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spSeudoMap: cell type mapping of spatial transcriptomics using unmatched single-cell RNA-seq data.

Bae Sungwoo S Choi Hongyoon H Lee Dong Soo DS

Genome medicine 20230317 1

Since many single-cell RNA-seq (scRNA-seq) data are obtained after cell sorting, such as when investigating immune cells, tracking cellular landscape by integrating single-cell data with spatial transcriptomic data is limited due to cell type and cell composition mismatch between the two datasets. We developed a method, spSeudoMap, which utilizes sorted scRNA-seq data to create virtual cell mixtures that closely mimic the gene expression of spatial data and trains a domain adaptation model for p ...[more]

PMID: 36932388

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