摘要
为提升黑色素瘤患者对免疫检查点抑制剂(ICI)治疗响应的预测准确性,提出了一种整合批量RNA测序和单细胞RNA测序数据的新方法。首先,通过皮尔逊相关性分析构建患者-细胞相关性矩阵,采用Louvain算法对单细胞RNA测序数据进行细胞分群;其次利用CellChat工具量化细胞群在免疫响应相关通路中的重要性;最后,通过引入基于细胞间通信网络构建的细胞群重要性评价准则,并结合群极小极大凹惩罚,提出了二重群极小极大凹惩罚Logistic回归模型(DMCPLR)。在GSE35640数据集上的实验表明,DMCPLR模型的预测准确率达到80.18%,精确率、召回率和F1分数分别为82.24%,89.71%和85.11%,显著优于包括Lasso回归和随机森林在内的14种对比方法的性能,同时,将致命错误率降至8.30%。消融分析实验证实,细胞群权重机制和L2正则化项的引入能够提高模型的性能。
To improve the accuracy of predicting the response of melanoma patients to immune checkpoint inhibitor(ICI)therapy,a new method integrating bulk RNA-seq and single-cell RNA-seq data was proposed.Firstly,a patient-cell correlation matrix was constructed through Pearson correlation analysis,and the Louvain algorithm was used to classify single-cell RNA-seq data into cell groups.The importance of cell groups in immune response related pathways was quantified using the CellChat tool.On this basis,a double group minimax concave penalty logistic regression model(DMCPLR)was proposed by introducing the cell group importance evaluation criterion constructed based on the cell-cell communication network and combining with the group minimax concave penalty.The experiments on the GSE35640 dataset showed that the prediction accuracy of the DMCPLR model reached 80.18%,with precision,recall,and F1 score of 82.24%,89.71%,and 85.11%,respectively,significantly better than the performance of 14 comparison methods including Lasso regression and random forest,while reducing the fatal error rate to 8.30%.The ablation analysis experiment confirmed that the introduction of cell group weight mechanism and L2 regularization term can improve the performance of the model.
作者
穆晓霞
张红梅
宋学坤
李钧涛
MU Xiaoxia;ZHANG Hongmei;SONG Xuekun;LI Juntao(College of Computer and Information Engineering,Henan Normal University,Xinxiang 453007,China;College of Life Sciences,Northeast Forestry University,Harbin 150006,China;College of Information Technology,Henan University of Chinese Medicine,Zhengzhou 450046,China;School of Mathematics and Statistics,Henan Normal University,Xinxiang 453007,China)
出处
《郑州大学学报(工学版)》
北大核心
2025年第6期58-65,共8页
Journal of Zhengzhou University(Engineering Science)
基金
国家自然科学基金资助项目(61203293)
河南省科技攻关项目(242102211023)。
