DEDUCE: Multi-head attention decoupled contrastive learning to discover cancer subtypes based on multi-omics data

• URL: http://arxiv.org/abs/2307.04075v3
• Date: Sat, 26 Oct 2024 07:43:46 GMT
• Title: DEDUCE: Multi-head attention decoupled contrastive learning to discover cancer subtypes based on multi-omics data
• Authors: Liangrui Pan, Xiang Wang, Qingchun Liang, Jiandong Shang, Wenjuan Liu, Liwen Xu, Shaoliang Peng,
• Abstract summary: We propose a model, named DEDUCE, for unsupervised contrastive learning to analyze multi-omics cancer data. This model adopts a unsupervised SMAE that can deeply extract contextual features and long-range dependencies from multi-omics data. Subtypes are clustered by calculating the similarity between samples in both the feature space and sample space of multi-omics data.
• Score: 7.049723871585993
• License:
• Abstract: Background and Objective: Given the high heterogeneity and clinical diversity of cancer, substantial variations exist in multi-omics data and clinical features across different cancer subtypes. Methods: We propose a model, named DEDUCE, based on a symmetric multi-head attention encoders (SMAE), for unsupervised contrastive learning to analyze multi-omics cancer data, with the aim of identifying and characterizing cancer subtypes. This model adopts a unsupervised SMAE that can deeply extract contextual features and long-range dependencies from multi-omics data, thereby mitigating the impact of noise. Importantly, DEDUCE introduces a subtype decoupled contrastive learning method based on a multi-head attention mechanism to simultaneously learn features from multi-omics data and perform clustering for identifying cancer subtypes. Subtypes are clustered by calculating the similarity between samples in both the feature space and sample space of multi-omics data. The fundamental concept involves decoupling various attributes of multi-omics data features and learning them as contrasting terms. A contrastive loss function is constructed to quantify the disparity between positive and negative examples, and the model minimizes this difference, thereby promoting the acquisition of enhanced feature representation. Results: The DEDUCE model undergoes extensive experiments on simulated multi-omics datasets, single-cell multi-omics datasets, and cancer multi-omics datasets, outperforming 10 deep learning models. The DEDUCE model outperforms state-of-the-art methods, and ablation experiments demonstrate the effectiveness of each module in the DEDUCE model. Finally, we applied the DEDUCE model to identify six cancer subtypes of AML.

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