Cancer Subtype Recognition Based on Laplacian Rank Constrained Multiview Clustering

Ge, Shuguang; Wang, Xuesong; Cheng, Yuhu; Liu, Jian

doi:10.3390/genes12040526

Cited by 3 publications

(2 citation statements)

References 42 publications

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“…MCCA is used as dimension reduction algorithms. We used the P values based on Cox log-rank model [43] to measure differential survival between the obtained subtype. With the decrease of P value, the subtype's survival rate is more significant, the clustering effect is more obvious.…”

Section: Comparison Algorithms and Evaluation Metricsmentioning

confidence: 99%

SADLN: Self-Attention Based Deep Learning Network of Integrating Multi-omics Data for Cancer Subtype Recognition

Gong

Sun

Cheng

et al. 2022

Preprint

Self Cite

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Background: Integrating multi-omics data for cancer subtype recognition is an important task in bioinformatics. Recently, deep learning has applied to recognize the subtype of cancers. However, existing studies almost integrate the multi-omics data simply by concatenation as the single data and then learn a latent low-dimensional representation through deep learning model, which didn't considering the distributes differently of omics data. Moreover, these methods ignore the relationship of samples. Results :In order to tackle these problems, we proposed SADLN: a self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition. SADLN combined encoder, self-attention, decoder, and discriminator into a unified framework, which can not only integrate multi-omics data but also adaptively model the sample’s relationship for learning a accurately latent low-dimensional representation. With the integrated representation learned from the network, SADLN used Gaussian Mixture Model to identify cancer subtypes. Experiments on ten cancer datasets of TCGA demonstrated the advantages of SADLN compared to ten methods. Conclusions: The Self-Attention Based Deep Learning Network (SADLN) is a effective method of integrate multi-omics data for cancer subtype recognition.

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Section: Comparison Algorithms and Evaluation Metricsmentioning

confidence: 99%

SADLN: Self-Attention Based Deep Learning Network of Integrating Multi-omics Data for Cancer Subtype Recognition

Gong

Sun

Cheng

et al. 2022

Preprint

Self Cite

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“…This problem can be solved by using spectral clustering (Ng et al, 2001). Spectral clustering is a classical data clustering method and widely used in multiview clustering algorithms (Nie et al, 2016;Kang et al, 2020;Feng et al, 2021;Ge et al, 2021) the effectiveness of MRF-MSC, cancer subtypes prediction experiments were carried out on TCGA data sets. The results showed that MRF-MSC was able to obtain more significant clinical differences in cancer typing.…”

Section: Introductionmentioning

confidence: 99%

Multi-View Spectral Clustering Based on Multi-Smooth Representation Fusion for Cancer Subtype Prediction

Liu

Cheng

et al. 2021

Front. Genet.

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It is a vital task to design an integrated machine learning model to discover cancer subtypes and understand the heterogeneity of cancer based on multiple omics data. In recent years, some multi-view clustering algorithms have been proposed and applied to the prediction of cancer subtypes. Among them, the multi-view clustering methods based on graph learning are widely concerned. These multi-view approaches usually have one or more of the following problems. Many multi-view algorithms use the original omics data matrix to construct the similarity matrix and ignore the learning of the similarity matrix. They separate the data clustering process from the graph learning process, resulting in a highly dependent clustering performance on the predefined graph. In the process of graph fusion, these methods simply take the average value of the affinity graph of multiple views to represent the result of the fusion graph, and the rich heterogeneous information is not fully utilized. To solve the above problems, in this paper, a Multi-view Spectral Clustering Based on Multi-smooth Representation Fusion (MRF-MSC) method was proposed. Firstly, MRF-MSC constructs a smooth representation for each data type, which can be viewed as a sample (patient) similarity matrix. The smooth representation can explicitly enhance the grouping effect. Secondly, MRF-MSC integrates the smooth representation of multiple omics data to form a similarity matrix containing all biological data information through graph fusion. In addition, MRF-MSC adaptively gives weight factors to the smooth regularization representation of each omics data by using the self-weighting method. Finally, MRF-MSC imposes constrained Laplacian rank on the fusion similarity matrix to get a better cluster structure. The above problems can be transformed into spectral clustering for solving, and the clustering results can be obtained. MRF-MSC unifies the above process of graph construction, graph fusion and spectral clustering under one framework, which can learn better data representation and high-quality graphs, so as to achieve better clustering effect. In the experiment, MRF-MSC obtained good experimental results on the TCGA cancer data sets.

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Multi-channel Partial Graph Integration Learning of Partial Multi-omics Data for Cancer Subtyping

Zhao

Cao

Zheng

2023

CBIO

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Background: The appearance of cancer subtypes with different clinical significance fully reflects the high heterogeneity of cancer. At present, the method of multi-omics integration has become more and more mature. However, in the practical application of the method, the omics of some samples are missing. Objective: The purpose of this study is to establish a depth model that can effectively integrate and express partial multi-omics data to accurately identify cancer subtypes. Methods: We proposed a novel partial multi-omics learning model for cancer subtypes, MPGIL (Multi-channel Partial Graph Integration Learning). MPGIL has two main components. Firstly, it obtains more lateral adjacency information between samples within the omics through the multi-channel graph autoencoders based on high-order proximity. To reduce the negative impact of missing samples, the weighted fusion layer is introduced to replace the concatenate layer to learn the consensus representation across multi-omics. Secondly, a classifier is introduced to ensure that the consensus representation is representative of clustering. Finally, subtypes were identified by K-means. Results: This study compared MPGIL with other multi-omics integration methods on 16 datasets. The clinical and survival results show that MPGIL can effectively identify subtypes. Three ablation experiments are designed to highlight the importance of each component in MPGIL. A case study of AML was conducted. The differentially expressed gene profiles among its subtypes fully reveal the high heterogeneity of cancer. Conclusion: MPGIL can effectively learn the consistent expression of partial multi-omics datasets and discover subtypes, and shows more significant performance than the state-of-the-art methods.

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Cancer Subtype Recognition Based on Laplacian Rank Constrained Multiview Clustering

Cited by 3 publications

References 42 publications

SADLN: Self-Attention Based Deep Learning Network of Integrating Multi-omics Data for Cancer Subtype Recognition

SADLN: Self-Attention Based Deep Learning Network of Integrating Multi-omics Data for Cancer Subtype Recognition

Multi-View Spectral Clustering Based on Multi-Smooth Representation Fusion for Cancer Subtype Prediction

Multi-channel Partial Graph Integration Learning of Partial Multi-omics Data for Cancer Subtyping

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