intCC: An efficient weighted integrative consensus clustering of multimodal data

Pac Symp Biocomput. 2024;29:627-640.ABSTRACTHigh 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.PMID:38160311 | PMC:PMC10764072

Source: Pacific Symposium on Biocomputing - December 31, 2023 Category: Bioinformatics Authors: Can Huang Pei Fen Kuan Source Type: research