intCC: An efficient weighted integrative consensus clustering of multimodal data

Details

• Alternative Title:
  Pac Symp Biocomput
• Personal Author:
  Huang, Can ; Kuan, Pei Fen
• Description:
  High throughput profiling of multiomics data provides a valuable resource to better understand the complex human disease such as cancer and to potentially uncover new subtypes. Integrative clustering has emerged as a powerful unsupervised learning framework for subtype discovery. In this paper, we propose an efficient weighted integrative clustering called intCC by combining ensemble method, consensus clustering and kernel learning integrative clustering. We illustrate that intCC can accurately uncover the latent cluster structures via extensive simulation studies and a case study on the TCGA pan cancer datasets. An R package intCC implementing our proposed method is available at https://github.com/candsj/intCC.
• Keywords:
  • Integrative clustering
  • Consensus clustering
  • Multiomics data
  • Ensemble learning
  • Humans
  • Computational Biology
  • Consensus
  • Algorithms
  • Cluster Analysis
  • Neoplasms
• Source:
  Pac Symp Biocomput. 29:627-640
• Pubmed ID:
  38160311
• Pubmed Central ID:
  PMC10764072
• Document Type:
  Journal Article
• Funding:
  U01 OH012257/OH/NIOSH CDC HHSUnited States/
• Volume:
  29
• Download URL:
  https://stacks.cdc.gov/view/cdc/140530/cdc_140530_DS1.pdf
• File Type:
  [PDF - 1.50 MB]
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:bc5f22633adab509f08daedd790fddea5569929cec493da142868a4d7fcb3582