Multi-view Subspace Clustering Analysis for Aggregating Multiple Heterogeneous Omics Data

Shi, Qianqian; Hu, Bing; Zeng, Tao; Zhang, Chuanchao

doi:10.3389/fgene.2019.00744

Cited by 14 publications

(15 citation statements)

References 35 publications

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“…As deep learning techniques have been widely used in the field of bioinformatics and achieved many successes, we added a deep learning-based method, Subtype-GAN [95], into our experiment. We notice that LRAcluster, PFA, and Mul-tiNMF are not originally designed for cancer subtyping problems but these methods represent general frameworks for integrating multi-omics data, which can be used to conduct different downstream analysis including cancer subtyping and have good performances [1,11,12,30]. For evaluation and comparison of more integration methods, we included these three methods in this study.…”

Section: Selection Of Integration Methodsmentioning

confidence: 99%

“…To understand and demonstrate the crucial need for addressing the second problem of data type selection, we surveyed 58 integration methods for cancer subtyping proposed from 2009 to 2019, and the result is summarized in Fig 1 where gene expression is treated as the same as mRNA expression and miRNA expression is placed into the group of epigenome based on observations from [7]. We summarized part of these 58 integration methods with the omics data they used in Fig 1A , and we can see, the data combinations used in these methods [2,[8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] are significantly inconsistent. For example, Fig 1B shows that while the mRNA expression data were used by 56 of the 58 methods, each of the other data types was only used by at most nearly half of these methods.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation and comparison of multi-omics data integration methods for cancer subtyping

Duan

Gao

et al. 2021

PLoS Comput Biol

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Computational integrative analysis has become a significant approach in the data-driven exploration of biological problems. Many integration methods for cancer subtyping have been proposed, but evaluating these methods has become a complicated problem due to the lack of gold standards. Moreover, questions of practical importance remain to be addressed regarding the impact of selecting appropriate data types and combinations on the performance of integrative studies. Here, we constructed three classes of benchmarking datasets of nine cancers in TCGA by considering all the eleven combinations of four multi-omics data types. Using these datasets, we conducted a comprehensive evaluation of ten representative integration methods for cancer subtyping in terms of accuracy measured by combining both clustering accuracy and clinical significance, robustness, and computational efficiency. We subsequently investigated the influence of different omics data on cancer subtyping and the effectiveness of their combinations. Refuting the widely held intuition that incorporating more types of omics data always produces better results, our analyses showed that there are situations where integrating more omics data negatively impacts the performance of integration methods. Our analyses also suggested several effective combinations for most cancers under our studies, which may be of particular interest to researchers in omics data analysis.

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Section: Selection Of Integration Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation and comparison of multi-omics data integration methods for cancer subtyping

Duan

Gao

et al. 2021

PLoS Comput Biol

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“…It is possible to identify the cell type from the whole gene regulatory network perspective (Li et al, 2017;Zheng et al, 2018Zheng et al, , 2019a. Besides, motivated by previous studies (Lan et al, 2018;Chen et al, 2019;Shi et al, 2019), multi-view learning and integrating with prior knowledge are promising directions to improve the accuracy of clustering and give a higher resolution of cell types.…”

Section: Discussionmentioning

confidence: 99%

An Adaptive Sparse Subspace Clustering for Cell Type Identification

et al. 2020

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The rapid development of single-cell transcriptome sequencing technology has provided us with a cell-level perspective to study biological problems. Identification of cell types is one of the fundamental issues in computational analysis of single-cell data. Due to the large amount of noise from single-cell technologies and high dimension of expression profiles, traditional clustering methods are not so applicable to solve it. To address the problem, we have designed an adaptive sparse subspace clustering method, called AdaptiveSSC, to identify cell types. AdaptiveSSC is based on the assumption that the expression of cells with the same type lies in the same subspace; one cell can be expressed as a linear combination of the other cells. Moreover, it uses a data-driven adaptive sparse constraint to construct the similarity matrix. The comparison results of 10 scRNA-seq datasets show that AdaptiveSSC outperforms original subspace clustering and other state-of-art methods in most cases. Moreover, the learned similarity matrix can also be integrated with a modified t-SNE to obtain an improved visualization result.

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“…However, these methods may further dilute the already low signal-to-noise ratio and increase the noise pollution to the results. Considering that the sample (patient) size of the biological data is much smaller than the feature (gene) size, some graph-based learning methods for cancer subtype recognition are designed [ 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. These methods use the sample points to quickly construct the similarity graph, which can be converted into the problem of spectral clustering.…”

Section: Introductionmentioning

confidence: 99%

Cancer Subtype Recognition Based on Laplacian Rank Constrained Multiview Clustering

Wang

Cheng

et al. 2021

Genes

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Integrating multigenomic data to recognize cancer subtype is an important task in bioinformatics. In recent years, some multiview clustering algorithms have been proposed and applied to identify cancer subtype. However, these clustering algorithms ignore that each data contributes differently to the clustering results during the fusion process, and they require additional clustering steps to generate the final labels. In this paper, a new one-step method for cancer subtype recognition based on graph learning framework is designed, called Laplacian Rank Constrained Multiview Clustering (LRCMC). LRCMC first forms a graph for a single biological data to reveal the relationship between data points and uses affinity matrix to encode the graph structure. Then, it adds weights to measure the contribution of each graph and finally merges these individual graphs into a consensus graph. In addition, LRCMC constructs the adaptive neighbors to adjust the similarity of sample points, and it uses the rank constraint on the Laplacian matrix to ensure that each graph structure has the same connected components. Experiments on several benchmark datasets and The Cancer Genome Atlas (TCGA) datasets have demonstrated the effectiveness of the proposed algorithm comparing to the state-of-the-art methods.

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Multi-view Subspace Clustering Analysis for Aggregating Multiple Heterogeneous Omics Data

Cited by 14 publications

References 35 publications

Evaluation and comparison of multi-omics data integration methods for cancer subtyping

Evaluation and comparison of multi-omics data integration methods for cancer subtyping

An Adaptive Sparse Subspace Clustering for Cell Type Identification

Cancer Subtype Recognition Based on Laplacian Rank Constrained Multiview Clustering

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