Graph domain adaptation-based framework for gene expression enhancement and cell type identification in large-scale spatially resolved transcriptomics.

ABSTRACT: Spatially resolved transcriptomics (SRT) technologies facilitate gene expression profiling with spatial resolution in a naïve state. Nevertheless, current SRT technologies exhibit limitations, manifesting as either low transcript detection sensitivity or restricted gene throughput. These constraints result in diminished precision and coverage in gene measurement. In response, we introduce SpaGDA, a sophisticated deep learning-based graph domain adaptation framework for both scenarios of gene expression imputation and cell type identification in spatially resolved transcriptomics data by impartially transferring knowledge from reference scRNA-seq data. Systematic benchmarking analyses across several SRT datasets generated from different technologies have demonstrated SpaGDA's superior effec

SUBMITTER: Shen R 

PROVIDER: S-EPMC11541786 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature