Integrated Robust Clustering of Low－grade Gliomas Multi－omics Data based on Deep Learning
10.11783/j.issn.1002－3674.2025.02.006
- VernacularTitle:基于深度学习的低级别胶质瘤多组学数据整合稳健分型
- Author:
Gang DU
1
;
Congcong JIA
;
Xin ZHAO
Author Information
1. 山西医科大学卫生统计教研室(030001)
- Publication Type:Journal Article
- Keywords:
Robust clustering;
Deep learning;
Multi－omics data;
Lower－grade gliomas
- From:
Chinese Journal of Health Statistics
2025;42(2):185-190
- CountryChina
- Language:Chinese
-
Abstract:
Objective We proposed a new method that combines autoencoder in deep learning with optimally tuned robust improper maximum likelihood estimator(OTRIMLE)for multi－omics data robust clustering,and further applied it to lower－grade gliomas(LGG)patients clustering.Methods The dimension of LGG's miRNA,mRNA and methylation data was reduced nonlinearly by autoencoder,and then OTRIMLE method was used for robust clustering.Cox proportional hazard model was conducted to evaluate the prognostic risk of different clusters,and differentially expressed miRNAs(DEmiRNAs),differentially expressed mRNAs(DEmRNAs)and differentially methylated genes(DMGs)among different clusters were screened out by differential expression analysis.GO enrichment analysis was performed on the overlapping genes of target genes of DEmiRNAs,DEmRNAs,and DMGs.Finally,we compared the level of infiltrating immune cells and pathway activity in different clusters.Results LGG patients were classified into four clusters,in which the risk of death of patients in cluster 4 was 5.903 times higher than that in cluster 3.8 DEmiRNAs,2890 DEmRNAs and 46 DMGs were identified,and 658 overlapping genes obtained by joint analysis were enriched in 423 GO items.13 pathways with different activity and 4 immune cells with different level of immune infiltration were screened out.Conclusion The OTRIMLE method based on deep learning can effectively handle noise,sparsity and outliers in multi－omics data,achieving robust clustering for LGG patients.The identified immune cells and pathways provide theoretical bases for the subsequent targeted treatment of LGG.
