Deeply integrating latent consistent representations in high-noise multi-omics data for cancer subtyping

Abstract Cancer is a complex and high-mortality disease regulated by multiple factors. Accurate cancer subtyping is crucial for formulating personalized treatment plans and improving patient survival rates. The underlying mechanisms that drive cancer progression can be comprehensively understood by...

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Bibliographic Details
Published in:Briefings in bioinformatics Vol. 25; no. 2
Main Authors: Cai, Yueyi, Wang, Shunfang
Format: Journal Article
Language:English
Published: England Oxford University Press 22.01.2024
Oxford Publishing Limited (England)
Subjects:
Algorithms
Cancer
Carcinoma, Renal Cell - genetics
Cluster Analysis
Clustering
Deep learning
Genomes
Humans
Integration
Kidney Neoplasms - genetics
Machine learning
Multiomics
Neoplasms - genetics
Noise levels
Problem Solving Protocol
Renal cell carcinoma
Representations
Survival
contrastive learning
multi-omics data integration
cancer subtyping
variational autoencoder
ISSN:1467-5463, 1477-4054, 1477-4054
Online Access:Get full text
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Summary:Abstract Cancer is a complex and high-mortality disease regulated by multiple factors. Accurate cancer subtyping is crucial for formulating personalized treatment plans and improving patient survival rates. The underlying mechanisms that drive cancer progression can be comprehensively understood by analyzing multi-omics data. However, the high noise levels in omics data often pose challenges in capturing consistent representations and adequately integrating their information. This paper proposed a novel variational autoencoder-based deep learning model, named Deeply Integrating Latent Consistent Representations (DILCR). Firstly, multiple independent variational autoencoders and contrastive loss functions were designed to separate noise from omics data and capture latent consistent representations. Subsequently, an Attention Deep Integration Network was proposed to integrate consistent representations across different omics levels effectively. Additionally, we introduced the Improved Deep Embedded Clustering algorithm to make integrated variable clustering friendly. The effectiveness of DILCR was evaluated using 10 typical cancer datasets from The Cancer Genome Atlas and compared with 14 state-of-the-art integration methods. The results demonstrated that DILCR effectively captures the consistent representations in omics data and outperforms other integration methods in cancer subtyping. In the Kidney Renal Clear Cell Carcinoma case study, cancer subtypes were identified by DILCR with significant biological significance and interpretability.
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbae061