由于许多单细胞RNA测序（scRNA-seq）数据是在细胞分选之后获得的，例如在研究免疫细胞时，通过将单细胞数据与空间转录组数据整合以跟踪细胞景观受到细胞类型和细胞组成两个数据集之间的限制。我们开发了一种方法，叫做spSeudoMap，利用排序后的scRNA-seq数据创建虚拟细胞混合物，其基因表达与空间数据非常相似，并训练域自适应模型以预测空间细胞组成。该方法在脑和乳腺癌组织中应用，并准确预测了细胞亚群的地形。spSeudoMap可以帮助澄清一些关键细胞类型的作用。©2023. 作者（们）。

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.© 2023. The Author(s).