Benchmark cancer datasets for Clustering algorithms for Omics-based Patient Stratification (COPS)

This repository contains seven multi-omic cancer datasets including several cancer types (breast, kidney, lung, ovary, prostate, and thyroid cancers as well as low grade gliomas) that were used for benchmarking several multi-view clustering algorithms implemented by COPS (https://github.com/UEFBiomedicalInformaticsLab/COPS). The datasets were originally compiled from The Cancer Genoma Atlas (TCGA) and downloaded using the curatedTCGAData R-package. The datasets include copy-number variations, methylomics as well as mRNA and miRNA transcriptomics. The methylomics data was mapped to genes by averaging methylation level of probes associated with the promoter regions of genes. Similarly the miRNA transcriptomics data was mapped to genes by using known and predicted miRNA -> gene interactions. Updated survival data was acquired from the Liu et al. 2018 paper. This repository also includes two sets of cancer associated pathway networks used by pathway-based multi-omic methods benchmarked in our study. NCI-PID pathways were downloaded using the ndexr R-package on December 22 2021. While KEGG pathways were downloaded using the pathview R-package on May 3 2022. More details on the processing can be found on the related publication.本仓库包含7个癌症多组学（multi-omic）数据集，涵盖多种癌症类型（乳腺癌、肾癌、肺癌、卵巢癌、前列腺癌、甲状腺癌以及低级别胶质瘤），这些数据集被用于基准测试由COPS（https://github.com/UEFBiomedicalInformaticsLab/COPS）实现的多款多视图聚类算法。 该数据集最初源自癌症基因组图谱（The Cancer Genome Atlas, TCGA），并通过curatedTCGAData R包下载获取。数据集包含拷贝数变异、甲基化组学数据，以及mRNA和miRNA转录组学数据。其中甲基化组学数据通过对与基因启动子区域相关的探针的甲基化水平取平均值，完成基因层面的映射；类似地，miRNA转录组学数据通过利用已知及预测的miRNA-基因相互作用关系，完成基因层面的映射。更新后的生存数据取自Liu等人2018年发表的研究论文。 本仓库还包含两套与癌症相关的通路网络，用于本研究中基准测试基于通路的多组学分析方法。其中NCI-PID通路数据集于2021年12月22日通过ndexr R包下载获取，而KEGG通路数据集则于2022年5月3日通过pathview R包下载获取。 有关数据处理的更多细节可查阅相关学术出版物。