clustering and survival analysis on multi-omics datasets

multi-omics data: the input data of the analysis, including miRNA, gene expression data, DNA methylation data, and survival outcome data. All the data were downloaded from TCGA.code: 1. data preprocessing. 2. clustering patients in each omics layer and performing Kaplan-Meier survival analysis to determine the association between patient clusters and survival outcomes. 3. differential expression analysis to identify features that are associated with patients with consistent survival outcomes.

多组学数据（multi-omics data）为本分析的输入数据，涵盖微小RNA（miRNA）、基因表达数据、DNA甲基化数据以及生存结局数据。所有数据均下载自癌症基因组图谱（The Cancer Genome Atlas, TCGA）。分析代码包含以下三个步骤：1. 数据预处理；2. 在每个组学层面对患者进行聚类，并开展Kaplan-Meier生存分析，以明确患者聚类结果与生存结局之间的关联；3. 差异表达分析，以识别与生存结局一致的患者群体相关的特征。