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.

中文翻译：

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一种改进的分层变分自动编码器，用于使用单细胞 RNA-seq 数据进行细胞间通信估计

分析肿瘤微环境中的细胞间通讯 (CCC) 有助于破译癌症进展和药物耐受性的潜在机制。目前，单细胞 RNA-Seq 数据可大规模获得，为预测细胞通信提供了前所未有的机会。基于已知的分子间相互作用，如配体、受体和细胞外基质，在推断细胞间通讯方面已经取得了许多成就和应用。然而，先验信息并不充分，只涉及一小部分细胞通信，产生许多假阳性或假阴性结果。为此，我们提出了一种改进的基于分层变分自动编码器 (HiVAE) 的模型，以充分利用单细胞 RNA-seq 数据自动估计 CCC。具体来说，HiVAE 模型用于分别学习已知配体-受体基因和单细胞 RNA-seq 数据中所有基因的细胞潜在表征，然后用于级联整合。随后，基于学习到的表示，传输熵被用来测量两个细胞之间的信息流传输，这被视为定向通信关系。分别对人类皮肤病数据集和黑色素瘤数据集的单细胞RNA-seq数据进行了实验。结果表明，HiVAE 模型在学习细胞表征方面是有效的，并且转移熵可用于估计细胞类型之间的通信分数。然后用于级联集成。随后，基于学习到的表示，传输熵被用来测量两个细胞之间的信息流传输，这被视为定向通信关系。分别对人类皮肤病数据集和黑色素瘤数据集的单细胞RNA-seq数据进行了实验。结果表明，HiVAE 模型在学习细胞表征方面是有效的，并且转移熵可用于估计细胞类型之间的通信分数。然后用于级联集成。随后，基于学习到的表示，传输熵被用来测量两个细胞之间的信息流传输，这被视为定向通信关系。分别对人类皮肤病数据集和黑色素瘤数据集的单细胞RNA-seq数据进行了实验。结果表明，HiVAE 模型在学习细胞表征方面是有效的，并且转移熵可用于估计细胞类型之间的通信分数。分别对人类皮肤病数据集和黑色素瘤数据集的单细胞RNA-seq数据进行了实验。结果表明，HiVAE 模型在学习细胞表征方面是有效的，并且转移熵可用于估计细胞类型之间的通信分数。分别对人类皮肤病数据集和黑色素瘤数据集的单细胞RNA-seq数据进行了实验。结果表明，HiVAE 模型在学习细胞表征方面是有效的，并且转移熵可用于估计细胞类型之间的通信分数。