ArticleStatistics in medicine2016
Integrative and regularized principal component analysis of multiple sources of data.
Article in Statistics in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Fast Fusion Clustering via Double Random Projection.Entropy (Basel, Switzerland) · 2024Article
- Robust integrative biclustering for multi-view data.Statistical methods in medical research · 2022Article
- Evaluation and comparison of multi-omics data integration methods for cancer subtyping.PLoS computational biology · 2021Article
- Integrative factorization of bidimensionally linked matrices.Biometrics · 2020Article
- Quantifying heterogeneity of expression data based on principal components.Bioinformatics (Oxford, England) · 2019Article
- Uncovering Large-Scale Conformational Change in Molecular Dynamics without Prior Knowledge.Journal of chemical theory and computation · 2016Article
- A New Algorithm and Theory for Penalized Regression-based Clustering.Journal of machine learning research : JMLR · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Integration of data of disparate types has become increasingly important to enhancing the power for new discoveries by combining complementary strengths of multiple types of data. One application is to uncover tumor subtypes in human cancer research in which multiple types of genomic data are integrated, including gene expression, DNA copy number, and DNA methylation data. In spite of their successes, existing approaches based on joint latent variable models require stringent distributional assumptions and may suffer from unbalanced scales (or units) of different types of data and non-scalability of the corresponding algorithms. In this paper, we propose an alternative based on integrative and regularized principal component analysis, which is distribution-free, computationally efficient, and robust against unbalanced scales. The new method performs dimension reduction simultaneously on multiple types of data, seeking data-adaptive sparsity and scaling. As a result, in addition to feature selection for each type of data, integrative clustering is achieved. Numerically, the proposed method compares favorably against its competitors in terms of accuracy (in identifying hidden clusters), computational efficiency, and robustness against unbalanced scales. In particular, compared with a popular method, the new method was competitive in identifying tumor subtypes associated with distinct patient survival patterns when applied to a combined analysis of DNA copy number, mRNA expression, and DNA methylation data in a glioblastoma multiforme study. Copyright © 2016 John Wiley & Sons, Ltd.
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Registered trials
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