Single-cell RNA sequencing (scRNA-seq) technologies have been heavily developed to probe gene expression profiles at single-cell resolution. Deep imputation methods have been proposed to address the related computational challenges (e.g. the gene sparsity in single-cell data). In particular, the neural architectures of those deep imputation models have been proven to be critical for performance. However, deep imputation architectures are difficult to design and tune for those without rich knowledge of deep neural networks and scRNA-seq. Therefore, Surrogate-assisted Evolutionary Deep Imputation Model (SEDIM) is proposed to automatically design the architectures of deep neural networks for imputing gene expression levels in scRNA-seq data without any manual tuning. Moreover, the proposed SEDIM constructs an offline surrogate model, which can accelerate the computational efficiency of the architectural search. Comprehensive studies show that SEDIM significantly improves the imputation and clustering performance compared with other benchmark methods. In addition, we also extensively explore the performance of SEDIM in other contexts and platforms including mass cytometry and metabolic profiling in a comprehensive manner. Marker gene detection, gene ontology enrichment and pathological analysis are conducted to provide novel insights into cell-type identification and the underlying mechanisms. The source code is available at https://github.com/li-shaochuan/SEDIM.

中文翻译：

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使用代理辅助自动深度学习的高通量单细胞 RNA-seq 数据插补和表征

单细胞 RNA 测序 (scRNA-seq) 技术已被大量开发用于以单细胞分辨率探测基因表达谱。已经提出了深度插补方法来解决相关的计算挑战（例如，单细胞数据中的基因稀疏性）。特别是，这些深度插补模型的神经架构已被证明对性能至关重要。然而，对于那些没有丰富的深度神经网络和 scRNA-seq 知识的人来说，深度插补架构很难设计和调整。因此，提出了代理辅助进化深度插补模型 (SEDIM) 来自动设计深度神经网络的架构，用于在无需任何手动调整的情况下插补 scRNA-seq 数据中的基因表达水平。此外，所提出的 SEDIM 构建了一个离线代理模型，这可以加速架构搜索的计算效率。综合研究表明，与其他基准方法相比，SEDIM 显着提高了插补和聚类性能。此外，我们还全面探索了 SEDIM 在其他环境和平台中的性能，包括大规模细胞术和代谢分析。进行标记基因检测、基因本体富集和病理学分析，为细胞类型鉴定和潜在机制提供新的见解。源代码可在 https://github.com/li-shaochuan/SEDIM 获得。我们还全面探索了 SEDIM 在其他环境和平台中的性能，包括大规模细胞术和代谢分析。进行标记基因检测、基因本体富集和病理学分析，为细胞类型鉴定和潜在机制提供新的见解。源代码可在 https://github.com/li-shaochuan/SEDIM 获得。我们还全面探索了 SEDIM 在其他环境和平台中的性能，包括大规模细胞术和代谢分析。进行标记基因检测、基因本体富集和病理学分析，为细胞类型鉴定和潜在机制提供新的见解。源代码可在 https://github.com/li-shaochuan/SEDIM 获得。