Recent advances in high-throughput single-cell RNA-seq have enabled us to measure thousands of gene expression levels at single-cell resolution. However, the transcriptomic profiles are high-dimensional and sparse in nature. To address it, a deep learning framework based on auto-encoder, termed DeepAE, is proposed to elucidate high-dimensional transcriptomic profiling data in an encode–decode manner. Comparative experiments were conducted on nine transcriptomic profiling datasets to compare DeepAE with four benchmark methods. The results demonstrate that the proposed DeepAE outperforms the benchmark methods with robust performance on uncovering the key dimensions of single-cell RNA-seq data. In addition, we also investigate the performance of DeepAE in other contexts and platforms such as mass cytometry and metabolic profiling in a comprehensive manner. Gene ontology enrichment and pathology analysis are conducted to reveal the mechanisms behind the robust performance of DeepAE by uncovering its key dimensions.

中文翻译：

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使用深度学习揭示高通量生物分子数据的关键维度。

高通量单细胞 RNA-seq 的最新进展使我们能够以单细胞分辨率测量数千个基因表达水平。然而，转录组谱本质上是高维且稀疏的。为了解决这个问题，提出了一种基于自动编码器的深度学习框架（称为 DeepAE），以编码解码方式阐明高维转录组分析数据。在九个转录组分析数据集上进行了比较实验，以将 DeepAE 与四种基准方法进行比较。结果表明，所提出的 DeepAE 在揭示单细胞 RNA-seq 数据的关键维度方面具有稳健的性能，优于基准方法。此外，我们还全面研究了 DeepAE 在其他环境和平台（例如质谱流式分析和代谢分析）中的性能。通过基因本体富集和病理学分析，通过揭示 DeepAE 的关键维度来揭示其稳健性能背后的机制。