Determining cell types by single-cell transcriptomics data is fundamental for downstream analysis. However, cell clustering and data imputation still face the computation challenges, due to the high dropout rate, sparsity and dimensionality of single-cell data. Although some deep learning based solutions have been proposed to handle these challenges, they still can not leverage gene attribute information and cell topology in a sensible way to explore the consistent clustering. In this paper, we present scDeepFC, a deep information fusion-based single-cell data clustering method for cell clustering and data imputation. Specifically, scDeepFC uses a deep auto-encoder (DAE) network and a deep graph convolution network to embed high-dimensional gene attribute information and high-order cell–cell topological information into different low-dimensional representations, and then fuses them to generate a more comprehensive and accurate consensus representation via a deep information fusion network. In addition, scDeepFC integrates the zero-inflated negative binomial (ZINB) into DAE to model the dropout events. By jointly optimizing the ZINB loss and cell graph reconstruction loss, scDeepFC generates a salient embedding representation for clustering cells and imputing missing data. Extensive experiments on real single-cell datasets prove that scDeepFC outperforms other popular single-cell analysis methods. Both the gene attribute and cell topology information can improve the cell clustering.

中文翻译：

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基于深度信息融合的单细胞 RNA-seq 数据聚类

通过单细胞转录组学数据确定细胞类型是下游分析的基础。然而，由于单细胞数据的高丢失率、稀疏性和维度，细胞聚类和数据插补仍然面临计算挑战。尽管已经提出了一些基于深度学习的解决方案来应对这些挑战，但它们仍然无法以合理的方式利用基因属性信息和细胞拓扑来探索一致的聚类。在本文中，我们提出了 scDeepFC，这是一种基于深度信息融合的单细胞数据聚类方法，用于细胞聚类和数据插补。具体来说，scDeepFC使用深度自动编码器（DAE）网络和深度图卷积网络将高维基因属性信息和高阶细胞-细胞拓扑信息嵌入到不同的低维表示中，然后将它们融合生成更全面的通过深度信息融合网络进行准确的共识表示。此外，scDeepFC 将零膨胀负二项式 (ZINB) 集成到 DAE 中以对 dropout 事件进行建模。通过联合优化 ZINB 损失和单元图重建损失，scDeepFC 生成用于聚类单元和插补缺失数据的显着嵌入表示。对真实单细胞数据集的大量实验证明，scDeepFC 优于其他流行的单细胞分析方法。