Single-cell RNA-sequencing (scRNA-seq) technologies have enabled the study of human cancers in individual cells, which explores the cellular heterogeneity and the genotypic status of tumors. Gene signature identification plays an important role in the precise classification of cancer subtypes. However, most existing gene selection methods only select the same informative genes for each subtype. In this study, we propose a novel gene selection method, automatic association feature learning (AAFL), which automatically identifies different gene signatures for different cell subpopulations (cancer subtypes) at the same time. The proposed AAFL method combines the residual network with the low-rank network, which selects genes that are most associated with the corresponding cell subpopulations. Moreover, the differential expression genes are acquired before gene selection to filter the redundant genes. We apply the proposed feature learning method to the real cancer scRNA-seq data sets (melanoma) to identify cancer subtypes and detect gene signatures of identified cancer subtypes. The experimental results demonstrate that the proposed method can automatically identify different gene signatures for identified cancer subtypes. Gene ontology enrichment analysis shows that the identified gene signatures of different subtypes reveal the key biological processes and pathways. These gene signatures are expected to bring important implications for understanding cellular heterogeneity and the complex ecosystem of tumors.

中文翻译：

---

AAFL：单细胞 RNA-seq 数据中癌症亚型基因签名识别的自动关联特征学习

单细胞 RNA 测序 (scRNA-seq) 技术使得在单个细胞中研究人类癌症成为可能，从而探索细胞异质性和肿瘤的基因型状态。基因特征识别在癌症亚型的精确分类中发挥着重要作用。然而，大多数现有的基因选择方法只为每个亚型选择相同的信息基因。在这项研究中，我们提出了一种新的基因选择方法，即自动关联特征学习（AAFL），它可以同时自动识别不同细胞亚群（癌症亚型）的不同基因特征。所提出的 AAFL 方法将残差网络与低秩网络相结合，选择与相应细胞亚群最相关的基因。此外，在基因选择之前获取差异表达基因，以过滤冗余基因。我们将所提出的特征学习方法应用于真实的癌症 scRNA-seq 数据集（黑色素瘤），以识别癌症亚型并检测已识别癌症亚型的基因特征。实验结果表明，所提出的方法可以自动识别已识别癌症亚型的不同基因特征。基因本体富集分析表明，不同亚型的识别基因特征揭示了关键的生物过程和途径。这些基因特征预计将为理解细胞异质性和复杂的肿瘤生态系统带来重要影响。