scDCA: deciphering the dominant cell communication assembly of downstream functional events from single-cell RNA-seq data.

Autor:	Ji B; College of Computer Science and Electronic Engineering, Hunan University, Yuelu, 410006 Changsha, China., Wang X; College of Computer Science and Electronic Engineering, Hunan University, Yuelu, 410006 Changsha, China., Wang X; The Second Xiangya Hospital, Central South University, Yuelu, 410006 Changsha, China., Xu L; College of Computer Science and Electronic Engineering, Hunan University, Yuelu, 410006 Changsha, China., Peng S; College of Computer Science and Electronic Engineering, Hunan University, Yuelu, 410006 Changsha, China.
Jazyk:	angličtina
Zdroj:	Briefings in bioinformatics [Brief Bioinform] 2024 Nov 22; Vol. 26 (1).
DOI:	10.1093/bib/bbae663
Abstrakt:	Cell-cell communications (CCCs) involve signaling from multiple sender cells that collectively impact downstream functional processes in receiver cells. Currently, computational methods are lacking for quantifying the contribution of pairwise combinations of cell types to specific functional processes in receiver cells (e.g. target gene expression or cell states). This limitation has impeded understanding the underlying mechanisms of cancer progression and identifying potential therapeutic targets. Here, we proposed a deep learning-based method, scDCA, to decipher the dominant cell communication assembly (DCA) that have a higher impact on a particular functional event in receiver cells from single-cell RNA-seq data. Specifically, scDCA employed a multi-view graph convolution network to reconstruct the CCCs landscape at single-cell resolution, and then identified DCA by interpreting the model with the attention mechanism. Taking the samples from advanced renal cell carcinoma as a case study, the scDCA was successfully applied and validated in revealing the DCA affecting the crucial gene expression in immune cells. The scDCA was also applied and validated in revealing the DCA responsible for the variation of 14 typical functional states of malignant cells. Furthermore, the scDCA was applied and validated to explore the alteration of CCCs under clinical intervention by comparing the DCA for certain cytotoxic factors between patients with and without immunotherapy. In summary, scDCA provides a valuable and practical tool for deciphering the cell type combinations with the most dominant impact on a specific functional process of receiver cells, which is of great significance for precise cancer treatment. Our data and code are free available at a public GitHub repository: https://github.com/pengsl-lab/scDCA.git. (© The Author(s) 2024. Published by Oxford University Press.)
Databáze:	MEDLINE
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