DGI算法在乳腺癌空间转录组学分析上的应用  

Application of DGI algorithm in spatial transcriptomic analysis of breast cancer

作　　者：尹娜 赵雅楠 尚文婧 司志好 冯振兴[1] YIN Na;ZHAO Ya'nan;SHANG Wenjing;SI Zhihao;FENG Zhenxing(College of Science,Inner Mongolia University of Technology,Hohhot 010051,China)

机构地区：[1]内蒙古工业大学理学院,呼和浩特010051

出　　处：《内蒙古工业大学学报（自然科学版）》2024年第6期489-494,共6页Journal of Inner Mongolia University of Technology：Natural Science Edition

基　　金：内蒙古自治区自然科学基金项目(2019BS03025)。

摘　　要：为了实现在单细胞水平上量化肿瘤空间异质性,选取10×Genomics平台上的乳腺癌空间转录组数据集为研究对象,使用深度图互信息(Deep graph infomax,DGI)模型对乳腺癌细胞进行聚类研究。结果显示,DGI算法展示出较好的聚类性能,调整兰德系数达到0.55,聚类结果接近人工注释分层且边界平滑,能够出色识别出乳腺癌标记基因和簇4与簇8之间的差异表达基因,富集结果表明这些基因与乳腺癌的发生发展有非常密切的关系。分析结果可能为乳腺癌患者找到作为临床诊断和治疗依据的标志物,对乳腺癌诊断和预后产生新的见解。In order to quantify the spatial heterogeneity of tumors at the single cell level,the spatial transcriptomic dataset of breast cancer on the 10×Genomics platform was selected as the study object,and deep graph infomax(DGI) model was used to cluster breast cancer cells.The results showed that the DGI algorithm showed good clustering performance,and the adjusted Rand index reached 0.55.The clustering results were close to manual annotation stratification,the boundary was smooth,and the breast cancer marker genes and differentially expressed genes between cluster 4 and cluster 8 were well identified.The enrichment results showed that these genes were closely related to the occurrence and development of breast cancer.The results of this analysis may provide new insights into the diagnosis and prognosis of breast cancer by identifying markers for clinical diagnosis and treatment of breast cancer patients.

关 键 词：乳腺癌 聚类 标记基因 差异基因 

分 类 号：Q61[生物学—生物物理学]