An improved hierarchical variational autoencoder for cell–cell communication estimation using single-cell RNA-seq data

Analysis of cell–cell communication (CCC) in the tumor micro-environment helps decipher the underlying mechanism of cancer progression and drug tolerance. Currently, single-cell RNA-Seq data are available on a large scale, providing an unprecedented opportunity to predict cellular communications. Th...

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Vydáno v:	Briefings in functional genomics Ročník 23; číslo 2; s. 118 - 127
Hlavní autoři:	Liu, Shuhui, Zhang, Yupei, Peng, Jiajie, Shang, Xuequn
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England 20.03.2024
Témata:	Cell Communication Exome Sequencing Gene Expression Profiling - methods Humans Neoplasms Sequence Analysis, RNA - methods Single-Cell Analysis - methods Single-Cell Gene Expression Analysis Tumor Microenvironment cell–cell communication single-cell RNA-seq data HiVAE model pairwise ligand–receptor transfer entropy
ISSN:	2041-2649, 2041-2657, 2041-2657
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Shrnutí:	Analysis of cell–cell communication (CCC) in the tumor micro-environment helps decipher the underlying mechanism of cancer progression and drug tolerance. Currently, single-cell RNA-Seq data are available on a large scale, providing an unprecedented opportunity to predict cellular communications. There have been many achievements and applications in inferring cell–cell communication based on the known interactions between molecules, such as ligands, receptors and extracellular matrix. However, the prior information is not quite adequate and only involves a fraction of cellular communications, producing many false-positive or false-negative results. To this end, we propose an improved hierarchical variational autoencoder (HiVAE) based model to fully use single-cell RNA-seq data for automatically estimating CCC. Specifically, the HiVAE model is used to learn the potential representation of cells on known ligand–receptor genes and all genes in single-cell RNA-seq data, respectively, which are then utilized for cascade integration. Subsequently, transfer entropy is employed to measure the transmission of information flow between two cells based on the learned representations, which are regarded as directed communication relationships. Experiments are conducted on single-cell RNA-seq data of the human skin disease dataset and the melanoma dataset, respectively. Results show that the HiVAE model is effective in learning cell representations, and transfer entropy could be used to estimate the communication scores between cell types.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2041-2649 2041-2657 2041-2657
DOI:	10.1093/bfgp/elac056