Due to the high heterogeneity and clinical characteristics of cancer, there
are significant differences in multi-omics data and clinical features among
subtypes of different cancers. Therefore, the identification and discovery of
cancer subtypes are crucial for the diagnosis, treatment, and prognosis of
cancer. In this study, we proposed a generalization framework based on
attention mechanisms for unsupervised contrastive learning (AMUCL) to analyze
cancer multi-omics data for the identification and characterization of cancer
subtypes. AMUCL framework includes a unsupervised multi-head attention
mechanism, which deeply extracts multi-omics data features. Importantly, a
decoupled contrastive learning model (DMACL) based on a multi-head attention
mechanism is proposed to learn multi-omics data features and clusters and
identify new cancer subtypes. This unsupervised contrastive learning method
clusters subtypes by calculating the similarity between samples in the feature
space and sample space of multi-omics data. Compared to 11 other deep learning
models, the DMACL model achieved a C-index of 0.002, a Silhouette score of
0.801, and a Davies Bouldin Score of 0.38 on a single-cell multi-omics dataset.
On a cancer multi-omics dataset, the DMACL model obtained a C-index of 0.016, a
Silhouette score of 0.688, and a Davies Bouldin Score of 0.46, and obtained the
most reliable cancer subtype clustering results for each type of cancer.
Finally, we used the DMACL model in the AMUCL framework to reveal six cancer
subtypes of AML. By analyzing the GO functional enrichment, subtype-specific
biological functions, and GSEA of AML, we further enhanced the interpretability
of cancer subtype analysis based on the generalizable AMUCL framework.

我们提出了一个基于注意力机制的无监督对比学习框架（AMUCL），用于分析癌症多组学数据以识别和表征癌症亚型。其中，基于多头注意力机制的 DMACL 模型用于学习多组学数据特征和聚类，并识别新的癌症亚型。通过计算特征空间和样本空间中样本之间的相似性，该无监督对比学习方法将亚型聚类。相较于其他 11 个深度学习模型，在单细胞多组学数据集上，DMACL 模型获得了 0.002 的 C-index、0.801 的轮廓系数和 0.38 的戴维森堡丁指数。在癌症多组学数据集上，DMACL 模型获得了 0.016 的 C-index、0.688 的轮廓系数和 0.46 的戴维森堡丁指数，为每种癌症类型提供了最可靠的癌症亚型聚类结果。最后，我们在 AMUCL 框架中使用 DMACL 模型揭示了 AML 的六个癌症亚型，并通过 GO 功能富集分析、亚型特定的生物学功能和 AML 的 GSEA 进一步提高了基于可推广的 AMUCL 框架的癌症亚型分析的解释能力。