Wang Liming, Liu Fangfang, Du Longting, Qin Guimin
School of Computer Science and Technology, Xidian University, Xi'an, China.
Front Genet. 2021 Jul 5;12:700036. doi: 10.3389/fgene.2021.700036. eCollection 2021.
Single-cell sequencing technology provides insights into the pathology of complex diseases like cancer. Here, we proposed a novel computational framework to explore the molecular mechanisms of cancer called melanoma. We first constructed a disease-specific cell-cell interaction network after data preprocessing and dimensionality reduction. Second, the features of cells in the cell-cell interaction network were learned by node2vec which is a graph embedding technology proposed previously. Then, consensus clusters were identified by considering different clustering algorithms. Finally, cell markers and cancer-related genes were further analyzed by integrating gene regulation pairs. We exploited our model on two independent datasets, which showed interesting results that the differences between clusters obtained by consensus clustering based on network embedding (CCNE) were observed obviously through visualization. For the KEGG pathway analysis of clusters, we found that all clusters are extremely related to MicroRNAs in cancer and HTLV-I infection, and the hub genes in cluster specific regulatory networks, i.e., , , , and are highly associated with melanoma. Furthermore, our method can also be extended to other scRNA-seq data.
单细胞测序技术为深入了解癌症等复杂疾病的病理学提供了思路。在此，我们提出了一种新颖的计算框架来探索名为黑色素瘤的癌症的分子机制。我们首先在数据预处理和降维后构建了一个疾病特异性的细胞 - 细胞相互作用网络。其次，通过之前提出的图嵌入技术node2vec来学习细胞 - 细胞相互作用网络中细胞的特征。然后，通过考虑不同的聚类算法来识别共识聚类。最后，通过整合基因调控对进一步分析细胞标志物和癌症相关基因。我们在两个独立数据集上应用了我们的模型，结果显示出有趣的现象，即通过基于网络嵌入的共识聚类（CCNE）获得的聚类之间的差异通过可视化可以明显观察到。对于聚类的KEGG通路分析，我们发现所有聚类都与癌症中的MicroRNAs和HTLV - I感染密切相关，并且聚类特异性调控网络中的枢纽基因，即 、 、 和 与黑色素瘤高度相关。此外，我们的方法还可以扩展到其他scRNA - seq数据。
