SADLN: Self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition

Sun, Qian; Cheng, Lei; Meng, Ao; Ge, Shuguang; Chen, Jie; Zhang, Longzhen; Gong, Ping

doi:10.3389/fgene.2022.1032768

Cited by 7 publications

(3 citation statements)

References 68 publications

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“…Although comprehensively analyzing multi-omics data can provide information at different omics levels, effectively integrating the consistent information from different omics data remains challenging [ 10 ]. Based on the stage of integration, existing methods can be classified into three categories: early integration methods, late integration methods and intermediate integration methods [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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

Cai,

Wang

2024

Briefings in Bioinformatics

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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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Section: Introductionmentioning

confidence: 99%

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

Cai,

Wang

2024

Briefings in Bioinformatics

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“…SADLN, on the other hand, utilized a self-attention mechanism to train and learn integrated latent features from multi-omics datasets. These features were subsequently employed as input for a Gaussian Mixture model to discern cancer subtypes effectively ( Sun et al, 2023 ). MOCDN presented self-attention-based neural network model to integrate three different omics profiles and identified biomarkers of kidney renal cell carcinoma ( Gong et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud

Choi,

Park,

Chae

2024

PeerJ

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Single-cell omics sequencing has rapidly advanced, enabling the quantification of diverse omics profiles at a single-cell resolution. To facilitate comprehensive biological insights, such as cellular differentiation trajectories, precise annotation of cell subtypes is essential. Conventional methods involve clustering cells and manually assigning subtypes based on canonical markers, a labor-intensive and expert-dependent process. Hence, an automated computational prediction framework is crucial. While several classification frameworks for predicting cell subtypes from single-cell RNA sequencing datasets exist, these methods solely rely on single-omics data, offering insights at a single molecular level. They often miss inter-omic correlations and a holistic understanding of cellular processes. To address this, the integration of multi-omics datasets from individual cells is essential for accurate subtype annotation. This article introduces moSCminer, a novel framework for classifying cell subtypes that harnesses the power of single-cell multi-omics sequencing datasets through an attention-based neural network operating at the omics level. By integrating three distinct omics datasets—gene expression, DNA methylation, and DNA accessibility—while accounting for their biological relationships, moSCminer excels at learning the relative significance of each omics feature. It then transforms this knowledge into a novel representation for cell subtype classification. Comparative evaluations against standard machine learning-based classifiers demonstrate moSCminer’s superior performance, consistently achieving the highest average performance on real datasets. The efficacy of multi-omics integration is further corroborated through an in-depth analysis of the omics-level attention module, which identifies potential markers for cell subtype annotation. To enhance accessibility and scalability, moSCminer is accessible as a user-friendly web-based platform seamlessly connected to a cloud system, publicly accessible at http://203.252.206.118:5568. Notably, this study marks the pioneering integration of three single-cell multi-omics datasets for cell subtype identification.

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“…In recent years, deep learning has become more and more widely used in the field of medical care and has become a popular method favored by researchers ( Dai et al, 2021 ). Many of these models have achieved good results in the field of cancer subtype identification, such as HI-DFN Forest ( Xu et al, 2019 ), Subtype-GAN ( Yang et al, 2021 ) and SADLN ( Sun et al, 2023 ). The HI-DFN Forest employs a stacked autoencoder to learn advanced representations from each omics data, and then integrates all the learned representations into an autoencoder layer to learn complex representations.…”

Section: Introductionmentioning

confidence: 99%

CAEM-GBDT: a cancer subtype identifying method using multi-omics data and convolutional autoencoder network

Shen,

Guo,

Bai

et al. 2024

Front. Bioinform.

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The identification of cancer subtypes plays a very important role in the field of medicine. Accurate identification of cancer subtypes is helpful for both cancer treatment and prognosis Currently, most methods for cancer subtype identification are based on single-omics data, such as gene expression data. However, multi-omics data can show various characteristics about cancer, which also can improve the accuracy of cancer subtype identification. Therefore, how to extract features from multi-omics data for cancer subtype identification is the main challenge currently faced by researchers. In this paper, we propose a cancer subtype identification method named CAEM-GBDT, which takes gene expression data, miRNA expression data, and DNA methylation data as input, and adopts convolutional autoencoder network to identify cancer subtypes. Through a convolutional encoder layer, the method performs feature extraction on the input data. Within the convolutional encoder layer, a convolutional self-attention module is embedded to recognize higher-level representations of the multi-omics data. The extracted high-level representations from the convolutional encoder are then concatenated with the input to the decoder. The GBDT (Gradient Boosting Decision Tree) is utilized for cancer subtype identification. In the experiments, we compare CAEM-GBDT with existing cancer subtype identifying methods. Experimental results demonstrate that the proposed CAEM-GBDT outperforms other methods. The source code is available from GitHub at https://github.com/gxh-1/CAEM-GBDT.git.

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SADLN: Self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition

Cited by 7 publications

References 68 publications

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

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

moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud

CAEM-GBDT: a cancer subtype identifying method using multi-omics data and convolutional autoencoder network

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