MOCSS: Multi-omics data clustering and cancer subtyping via shared and specific representation learning

Chen, Yuxin; Wen, Yao; Xie, Chenyang; Chen, Xinjian; He, Song; Bo, Xiaochen; Zhang, Zhongnan

doi:10.1016/j.isci.2023.107378

Cited by 7 publications

(5 citation statements)

References 52 publications

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“…We have observed, however, that many of the best projections are associated with nearly orthogonal bases. This indicates that the encoder-decoder dimensionality reduction strategy can be successfully coupled with an orthogonality constraint 42 , 43 in future research.…”

Section: Resultsmentioning

confidence: 94%

Improving reduced-order models through nonlinear decoding of projection-dependent outputs

Zdybał,

Parente,

Sutherland

2023

Patterns

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Section: Resultsmentioning

confidence: 94%

Improving reduced-order models through nonlinear decoding of projection-dependent outputs

Zdybał,

Parente,

Sutherland

2023

Patterns

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“…These include the early integration method LRAcluster [ 13 ], the late integration method PINSPlus [ 16 ] and 12 intermediate integration methods. The intermediate integration methods include six traditional methods and six latest deep learning integration methods: SNF [ 21 ], rMKL-LPP [ 17 ], MCCA [ 14 ], MultiNMF [ 15 ], iClusterBayes [ 12 ], NEMO [ 22 ], DCAP [ 29 ], DLSF [ 30 ], DSIR [ 31 ], MRGCN [ 32 ], MOCSS [ 37 ] and DMCL [ 38 ]. Among them, DCAP, DLSF, MOCSS and DMCL are deep learning integration methods designed to solve the noise in heterogeneous omics data.…”

Section: Resultsmentioning

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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“…To assess Subtype-MGTP’s performance in cancer subtype identification, we conducted extensive experiments on the benchmark datasets, comparing it against nine state-of-the-art methods: K-means ( Ding and He 2004 ), LRAcluster ( Wu et al 2015 ), PINS ( Nguyen et al 2017 ), MCCA ( Witten and Tibshirani 2009 ), NEMO ( Rappoport and Shamir 2019 ), Subtype-GAN ( Yang et al 2021 ), Subtype-DCC ( Zhao et al 2023 ), DMCL ( Chen et al 2023a ), and MOCSS ( Chen et al 2023b ). To ensure a fair comparison among all methods, a consistent number of clusters was set for each dataset, adhering to established research guidelines ( Yang et al 2021 ).…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, two novel end-to-end multi-omics cancer subtyping models Subtype-DCC ( Zhao et al 2023 ) and DMCL ( Chen et al 2023a ) optimize representation learning and clustering jointly. MOCSS ( Chen et al 2023b ) integrated improved contrastive learning techniques and orthogonality constraints to fully explore both omics-specific learning and cross-omics correlation learning.…”

Section: Introductionmentioning

confidence: 99%

Subtype-MGTP: a cancer subtype identification framework based on multi-omics translation

Xie,

Kuang,

Song

et al. 2024

Bioinformatics

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Motivation The identification of cancer subtypes plays a crucial role in cancer research and treatment. With the rapid development of high-throughput sequencing technologies, there has been an exponential accumulation of cancer multi-omics data. Integrating multi-omics data has emerged as a cost-effective and efficient strategy for cancer subtyping. While current methods primarily rely on genomics data, protein expression data offers a closer representation of phenotype. Therefore, integrating protein expression data holds promise for enhancing subtyping accuracy. However, the scarcity of protein expression data compared to genomics data presents a challenge in its direct incorporation into existing methods. Moreover, striking a balance between omics-specific learning and cross-omics learning remains a prevalent challenge in current multi-omics integration methods. Results We introduce Subtype-MGTP, a novel cancer subtyping framework based on the translation of Multiple Genomics To Proteomics. Subtype-MGTP comprises two modules: a translation module, which leverages available protein data to translate multi-type genomics data into predicted protein expression data, and an improved deep subspace clustering module, which integrates contrastive learning to cluster the predicted protein data, yielding refined subtyping results. Extensive experiments conducted on benchmark datasets demonstrate that Subtype-MGTP outperforms nine state-of-the-art cancer subtyping methods. The interpretability of clustering results is further supported by the clinical and survival analysis. Subtype-MGTP also exhibits strong robustness against varying rates of missing protein data and demonstrates distinct advantages in integrating multi-omics data with imbalanced multi-omics data. Availability and implementation The code and results are available at https://github.com/kybinn/Subtype-MGTP.

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MOCSS: Multi-omics data clustering and cancer subtyping via shared and specific representation learning

Cited by 7 publications

References 52 publications

Improving reduced-order models through nonlinear decoding of projection-dependent outputs

Improving reduced-order models through nonlinear decoding of projection-dependent outputs

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

Subtype-MGTP: a cancer subtype identification framework based on multi-omics translation

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