.png){kind=icon}
.png){kind=icon}
Return
Deciphering cell-cell communication at single-cell resolution for spatial transcriptomics with subgraph-based graph attention network
.png){kind=icon}
OA
AI
DOI：10.1038/s41467-024-51329-2.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The inference of cell-cell communication (CCC) is crucial for a better understanding of complex cellular dynamics and regulatory mechanisms in biological systems. However, accurately inferring spatial CCCs at single-cell resolution remains a significant challenge. To address this issue, we present a versatile method, called DeepTalk, to infer spatial CCC at single-cell resolution by integrating single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data. DeepTalk utilizes graph attention network (GAT) to integrate scRNA-seq and ST data, which enables accurate cell-type identification for single-cell ST data and deconvolution for spot-based ST data. Then, DeepTalk can capture the connections among cells at multiple levels using subgraph-based GAT, and further achieve spatially resolved CCC inference at single-cell resolution. DeepTalk achieves excellent performance in discovering meaningful spatial CCCs on multiple cross-platform datasets, which demonstrates its superior ability to dissect cellular behavior within intricate biological processes. cell-cell communication (CCC) is crucial for understanding biological processes. Here, authors present DeepTalk, which combines single-cell RNA sequencing and spatial transcriptomics data to infer cell-cell communication at single-cell resolution, revealing intricate intercellular dynamics within tissues.
Keywords:
GENOME-WIDE EXPRESSION
RNA-SEQ
RECEPTOR
GENE
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
15.7
Papers:
9.2W
Citations:
91.2W
Organization
View more organizations