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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.

Affiliations

Sungwoo Bae 1, Hongyoon Choi 2 3 4, Dong Soo Lee 5 6
1Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Republic of Korea.
2Department of Nuclear Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. chy1000@snu.ac.kr.
3Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea. chy1000@snu.ac.kr.
4Portrai, Inc., Seoul, Republic of Korea. chy1000@snu.ac.kr.
5Department of Nuclear Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea. dsl@plaza.snu.ac.kr.
6Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea. dsl@plaza.snu.ac.kr.