Application of single-cell/nucleus genomic sequencing to patient-derived tissues offers potential solutions to delineate disease mechanisms in human. However, individual cells in patient-derived tissues are in different pathological stages, and hence such cellular variability impedes subsequent differential gene expression analyses. To overcome such heterogeneity issue, we present a novel deep learning approach, scIDST, that infers disease progressive levels of individual cells with weak supervision framework. The inferred disease progressive cells displayed significant differential expression of disease-relevant genes, which could not be detected by comparative analysis between patients and healthy donors. In addition, we demonstrated that pretrained models by scIDST are applicable to multiple independent data resources, and advantageous to infer cells related to certain disease risks and comorbidities. Taken together, scIDST offers a new strategy of single-cell sequencing analysis to identify bona fide disease-associated molecular features.

中文翻译：

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使用弱监督深度学习方法推断单细胞转录组学中的疾病进展阶段


将单细胞/细胞核基因组测序应用于患者来源的组织为描绘人类疾病机制提供了潜在的解决方案。然而，患者来源组织中的单个细胞处于不同的病理阶段，因此这种细胞变异性阻碍了随后的差异基因表达分析。为了克服这种异质性问题，我们提出了一种新的深度学习方法 scIDST，它可以推断具有较弱监督框架的单个细胞的疾病进展水平。推断的疾病进展细胞表现出疾病相关基因的显著差异表达，无法通过患者与健康供体之间的比较分析来检测。此外，我们证明 scIDST 的预训练模型适用于多个独立的数据资源，并且有利于推断与某些疾病风险和合并症相关的细胞。综上所述，scIDST 提供了一种新的单细胞测序分析策略，以识别真正的疾病相关分子特征。