CellDART: cell type inference by domain adaptation of single-cell and spatial transcriptomic data.

ABSTRACT: Deciphering the cellular composition in genome-wide spatially resolved transcriptomic data is a critical task to clarify the spatial context of cells in a tissue. In this study, 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 it 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. First, CellDART was applied to a mouse brain and a human dorsolateral prefrontal cortex tissue to identify cell types with a layer-specific spatial distribution. Overall, the proposed approach showed more st

SUBMITTER: Bae S 

PROVIDER: S-EPMC9177989 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature