ArticleBiometrika2020
Sparse semiparametric canonical correlation analysis for data of mixed types.
Article in Biometrika, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Sparse Gaussianized Canonical Correlation Analysis with Applications to Portfolio Analysis.Journal of the American Statistical Association · 2026Article
- Truncated Gaussian copula principal component analysis with application to pediatric acute lymphoblastic leukemia patients' gut microbiome.Statistical methods in medical research · 2026Article
- Artificial intelligence to investigate metabolomics data for precision medicine.Metabolomics : Official journal of the Metabolomic Society · 2026Review
- Deep Subspace Reconstruction-Driven Adaptive Canonical Correlation Analysis Model for Identifying Imaging Genetics Associations in Sarcoma.Applied biochemistry and biotechnology · 2025Article
- Asymmetric canonical correlation analysis of Riemannian and high-dimensional data.Electronic journal of statistics · 2025Article
- Sparse Semiparametric Discriminant Analysis for High-dimensional Zero-inflated Data.Journal of machine learning research : JMLR · 2025Article
- Functional Principal Component Analysis for Continuous Non-Gaussian, Truncated, and Discrete Functional Data.Statistics in medicine · 2024Article
- OneNet-One network to rule them all: Consensus network inference from microbiome data.PLoS computational biology · 2024Article
- Measuring symptoms of obsessive-compulsive and related disorders using a single dimensional self-report scale.Frontiers in psychiatry · 2023Article
- Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering.PLoS computational biology · 2023Article
- Ten quick tips for biomarker discovery and validation analyses using machine learning.PLoS computational biology · 2022Article
- Modeling Microbial Community Networks: Methods and Tools.Current genomics · 2021Review
- NetCoMi: network construction and comparison for microbiome data in R.Briefings in bioinformatics · 2021Article
- Canonical correlation analysis for elliptical copulas.Journal of multivariate analysis · 2021Article
- Fast computation of latent correlations.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2021Article
- Shrinkage improves estimation of microbial associations under different normalization methods.NAR genomics and bioinformatics · 2020Article
- Microbial Networks in SPRING - Semi-parametric Rank-Based Correlation and Partial Correlation Estimation for Quantitative Microbiome Data.Frontiers in genetics · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Canonical correlation analysis investigates linear relationships between two sets of variables, but often works poorly on modern datasets due to high-dimensionality and mixed data types (continuous/binary/zero-inflated). We propose a new approach for sparse canonical correlation analysis of mixed data types that does not require explicit parametric assumptions. Our main contribution is the use of truncated latent Gaussian copula to model the data with excess zeroes, which allows us to derive a rank-based estimator of latent correlation matrix without the estimation of marginal transformation functions. The resulting semiparametric sparse canonical correlation analysis method works well in high-dimensional settings as demonstrated via numerical studies, and application to the analysis of association between gene expression and micro RNA data of breast cancer patients.
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