Single-Cell Transcriptome Analysis in Melanoma Using Network Embedding - Wang, Liming; Liu, Fangfang; Du, Longting; Qin, Guimin (Frontiers in Genetics, 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 firstly constructed a disease-specific cell-cell interaction network after data preprocessing and dimensionality reduction. Secondly, the features of cells in the cell-cell interaction network were learned by node2vec which is a graph embedding technology proposed previously. Then, consensus cell types 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 show interesting results that the differences between cell types obtained by CCNE were observed obviously through visualization. For the KEGG pathway analysis of cell types, we found that all cell types are extremely related to MicroRNAs in cancer and HTLV-I infection, and the hub genes in cell-type specific regulatory networks, i.e. ETS1, TP53, E2F1 and GATA3 are highly associated with melanoma. Furthermore, our method can also be extended to other scRNA-seq data.