COMSUC: A web server for the identification of consensus molecular subtypes of cancer based on multiple methods and multi-omics data

被引:0
|
作者
He S. [1 ]
Song X. [2 ]
Yang X. [1 ,3 ]
Yu J. [4 ]
Wen Y. [1 ]
Wu L. [1 ]
Yan B. [1 ]
Feng J. [4 ]
Bo X. [1 ]
机构
[1] Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing
[2] Key Laboratory of Biomedical Engineering and Translational Medicine, Ministry of Industry and Information Technology, Chinese PLA General Hospital, Beijing
[3] Experimental Center, Beijing Friendship Hospital, Capital Medical University, Beijing
[4] State key Laboratory of Toxicology and Medical Countermeasures, Beijing Institute of Pharmacology and Toxicology, Beijing
来源
PLoS Computational Biology | 2021年 / 17卷 / 03期
关键词
D O I
10.1371/JOURNAL.PCBI.1008769
中图分类号
学科分类号
摘要
Extensive amounts of multi-omics data and multiple cancer subtyping methods have been developed rapidly, and generate discrepant clustering results, which poses challenges for cancer molecular subtype research. Thus, the development of methods for the identification of cancer consensus molecular subtypes is essential. The lack of intuitive and easy-to-use analytical tools has posed a barrier. Here, we report on the development of the COnsensus Molecular SUbtype of Cancer (COMSUC) web server. With COMSUC, users can explore consensus molecular subtypes of more than 30 cancers based on eight clustering methods, five types of omics data from public reference datasets or users' private data, and three consensus clustering methods. The web server provides interactive and modifiable visualization, and publishable output of analysis results. Researchers can also exchange consensus subtype results with collaborators via project IDs. COMSUC is now publicly and freely available with no login requirement at http://comsuc.bioinforai.tech/ (IP address: http://59.110.25.27/). For a video summary of this web server, see S1 Video and S1 File. © 2021 He et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
引用
下载
收藏
相关论文
共 50 条
  • [21] CAMOIP: a web server for comprehensive analysis on multi-omics of immunotherapy in pan-cancer
    Lin, Anqi
    Qi, Chang
    Wei, Ting
    Li, Mengyao
    Cheng, Quan
    Liu, Zaoqu
    Luo, Peng
    Zhang, Jian
    BRIEFINGS IN BIOINFORMATICS, 2022, 23 (03)
  • [22] A Deep Learning Fusion Clustering framework for breast cancer subtypes identification by integrating multi-omics data
    Liu Shuangshuang
    Qi Lin
    Tie Yun
    Liu Fenghui
    2020 5TH INTERNATIONAL CONFERENCE ON MECHANICAL, CONTROL AND COMPUTER ENGINEERING (ICMCCE 2020), 2020, : 1710 - 1714
  • [23] Classifying Breast Cancer Subtypes Using Deep Neural Networks Based on Multi-Omics Data
    Lin, Yuqi
    Zhang, Wen
    Cao, Huanshen
    Li, Gaoyang
    Du, Wei
    GENES, 2020, 11 (08) : 1 - 18
  • [24] Multiple machine learning-based integrations of multi-omics data to identify molecular subtypes and construct a prognostic model for HNSCC
    Xiaoqin Luo
    Chao Li
    Gang Qin
    Hereditas, 162 (1)
  • [25] Identification of Diagnostic Biomarkers and Subtypes of Liver Hepatocellular Carcinoma by Multi-Omics Data Analysis
    Ouyang, Xiao
    Fan, Qingju
    Ling, Guang
    Shi, Yu
    Hu, Fuyan
    GENES, 2020, 11 (09) : 1 - 18
  • [26] Identification of molecular features correlating with tumor immunity in gastric cancer by multi-omics data analysis
    He, Yin
    Wang, Xiaosheng
    ANNALS OF TRANSLATIONAL MEDICINE, 2020, 8 (17)
  • [27] Integrative clustering methods of multi-omics data for molecule-based cancer classifications
    Dongfang Wang
    Jin Gu
    Quantitative Biology, 2016, 4 (01) : 58 - 67
  • [28] ProgCAE: a deep learning-based method that integrates multi-omics data to predict cancer subtypes
    Liu, Qingchun
    Song, Kai
    BRIEFINGS IN BIOINFORMATICS, 2023, 24 (04)
  • [29] Evaluation and comparison of multi-omics data integration methods for cancer subtyping
    Duan, Ran
    Gao, Lin
    Gao, Yong
    Hu, Yuxuan
    Xu, Han
    Huang, Mingfeng
    Song, Kuo
    Wang, Hongda
    Dong, Yongqiang
    Jiang, Chaoqun
    Zhang, Chenxing
    Jia, Songwei
    PLOS COMPUTATIONAL BIOLOGY, 2021, 17 (08)
  • [30] A Similarity Regression Fusion Model for Integrating Multi-Omics Data to Identify Cancer Subtypes
    Guo, Yang
    Zheng, Jianning
    Shang, Xuequn
    Li, Zhanhuai
    GENES, 2018, 9 (07):