GTAD: a graph-based approach for cell spatial composition inference from integrated scRNA-seq and ST-seq data.

ABSTRACT: With the emergence of spatial transcriptome sequencing (ST-seq), research now heavily relies on the joint analysis of ST-seq and single-cell RNA sequencing (scRNA-seq) data to precisely identify cell spatial composition in tissues. However, common methods for combining these datasets often merge data from multiple cells to generate pseudo-ST data, overlooking topological relationships and failing to represent spatial arrangements accurately. We introduce GTAD, a method utilizing the Graph Attention Network for deconvolution of integrated scRNA-seq and ST-seq data. GTAD effectively captures cell spatial relationships and topological structures within tissues using a graph-based approach, enhancing cell-type identification and our understanding of complex tissue cellular landscapes. By integ

SUBMITTER: Zhang T 

PROVIDER: S-EPMC10734610 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature