GSE172416
Cell type inference in human lung tissue by domain adaptation of single-cell and spatial transcriptomic data
We developed a method, CellDART, which estimates the spatial distribution of cells defined by single-cell level data using domain adaptation of neural networks, and applied to the spatial mapping of human lung tissue. The neural network that predicts the cell proportion in a pseudospot, a virtual mixture of cells from single-cell data, is translated to decompose the cell types in each spatial barcoded region. CellDART elucidated the cell type predominance defined by the human lung cell atlas across the human lung tissue compartments and it corresponded to the known prevalent cell types. Overall design: Two normal lung samples were acquired from lung specimen from one patient who underwent surgical resection for lung cancer. The samples were cryosectioned and processed for Visium Spatial Transcritpomic analysis.