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
  • [31] DEDUCE: Multi-head attention decoupled contrastive learning to discover cancer subtypes based on multi-omics data
    Pan, Liangrui
    Wang, Xiang
    Liang, Qingchun
    Shang, Jiandong
    Liu, Wenjuan
    Xu, Liwen
    Peng, Shaoliang
    Computer Methods and Programs in Biomedicine, 2024, 257
  • [32] Transcriptome profiling-based identification of prognostic subtypes and multi-omics signatures of glioblastoma
    Junseong Park
    Jin-Kyoung Shim
    Seon-Jin Yoon
    Se Hoon Kim
    Jong Hee Chang
    Seok-Gu Kang
    Scientific Reports, 9
  • [33] Transcriptome profiling-based identification of prognostic subtypes and multi-omics signatures of glioblastoma
    Park, Junseong
    Shim, Jin-Kyoung
    Yoon, Seon-Jin
    Kim, Se Hoon
    Chang, Jong Hee
    Kang, Seok-Gu
    SCIENTIFIC REPORTS, 2019, 9 (1)
  • [34] Mergeomics 2.0: a web server for multi-omics data integration to elucidate disease networks and predict therapeutics
    Ding, Jessica
    Blencowe, Montgomery
    Thien Nghiem
    Ha, Sung-min
    Chen, Yen-Wei
    Li, Gaoyan
    Yang, Xia
    NUCLEIC ACIDS RESEARCH, 2021, 49 (W1) : W375 - W387
  • [35] Editorial: Identification of immune-related biomarkers for cancer diagnosis based on multi-omics data
    Cheng, Liang
    Zhang, Xin
    Li, Chuan-Xin
    Guo, Rui
    Zhao, Tianyi
    FRONTIERS IN ONCOLOGY, 2022, 12
  • [36] A benchmark study of deep learning-based multi-omics data fusion methods for cancer
    Dongjin Leng
    Linyi Zheng
    Yuqi Wen
    Yunhao Zhang
    Lianlian Wu
    Jing Wang
    Meihong Wang
    Zhongnan Zhang
    Song He
    Xiaochen Bo
    Genome Biology, 23
  • [37] A benchmark study of deep learning-based multi-omics data fusion methods for cancer
    Leng, Dongjin
    Zheng, Linyi
    Wen, Yuqi
    Zhang, Yunhao
    Wu, Lianlian
    Wang, Jing
    Wang, Meihong
    Zhang, Zhongnan
    He, Song
    Bo, Xiaochen
    GENOME BIOLOGY, 2022, 23 (01)
  • [38] Integrated multi-omics data reveals the molecular subtypes and guides the androgen receptor signalling inhibitor treatment of prostate cancer
    Meng, Jialin
    Lu, Xiaofan
    Jin, Chen
    Zhou, Yujie
    Ge, Qintao
    Zhou, Jun
    Hao, Zongyao
    Yan, Fangrong
    Zhang, Meng
    Liang, Chaozhao
    CLINICAL AND TRANSLATIONAL MEDICINE, 2021, 11 (12):
  • [39] Multi-omics integration with weighted affinity and self-diffusion applied for cancer subtypes identification
    Duan, Xin
    Ding, Xinnan
    Zhao, Zhuanzhe
    JOURNAL OF TRANSLATIONAL MEDICINE, 2024, 22 (01)
  • [40] Multi-omics integration with weighted affinity and self-diffusion applied for cancer subtypes identification
    Xin Duan
    Xinnan Ding
    Zhuanzhe Zhao
    Journal of Translational Medicine, 22