Cancer Subtype Identification based on Multi-view Subspace Clustering with Adaptive Local Structure Learning

Causal effect evaluation and causal network learning are two main research areas in causal inference. For causal effect evaluation, we review the two problems of confounders and surrogates. The Yule-Simpson paradox is the idea that the association between two variables may be changed dramatically due to ignoring confounders. We review criteria for confounders and methods of adjustment for observed and unobserved confounders. The surrogate paradox occurs when a treatment has a positive causal effect on a surrogate endpoint, which, in turn, has a positive causal effect on a true endpoint, but the treatment may have a negative causal effect on the true endpoint. Some of the existing criteria for surrogates are subject to the surrogate paradox, and we review criteria for consistent surrogates to avoid the surrogate paradox. Causal networks are used to depict the causal relationships among multiple variables. Rather than discovering a global causal network, researchers are often interested in discovering the causes and effects of a given variable. We review some algorithms for local structure learning of causal networks centering around a given variable.

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Integrative subspace clustering by common and specific decomposition for applications on cancer subtype identification

BMC Medical Genomics ◽

10.1186/s12920-019-0633-1 ◽

2019 ◽

Vol 12 (S9) ◽

Author(s):

Yin Guo ◽

Huiran Li ◽

Menglan Cai ◽

Limin Li

Keyword(s):

Main Idea ◽

Subspace Clustering ◽

Matrix Decomposition ◽

Specific Information ◽

Clustering Methods ◽

Common Information ◽

Cancer Subtype ◽

Subtype Identification ◽

Cancer Types ◽

Different Sources

Abstract Background Recent high throughput technologies have been applied for collecting heterogeneous biomedical omics datasets. Computational analysis of the multi-omics datasets could potentially reveal deep insights for a given disease. Most existing clustering methods by multi-omics data assume strong consistency among different sources of datasets, and thus may lose efficacy when the consistency is relatively weak. Furthermore, they could not identify the conflicting parts for each view, which might be important in applications such as cancer subtype identification. Methods In this work, we propose an integrative subspace clustering method (ISC) by common and specific decomposition to identify clustering structures with multi-omics datasets. The main idea of our ISC method is that the original representations for the samples in each view could be reconstructed by the concatenation of a common part and a view-specific part in orthogonal subspaces. The problem can be formulated as a matrix decomposition problem and solved efficiently by our proposed algorithm. Results The experiments on simulation and text datasets show that our method outperforms other state-of-art methods. Our method is further evaluated by identifying cancer types using a colorectal dataset. We finally apply our method to cancer subtype identification for five cancers using TCGA datasets, and the survival analysis shows that the subtypes we found are significantly better than other compared methods. Conclusion We conclude that our ISC model could not only discover the weak common information across views but also identify the view-specific information.

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