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Sparse semiparametric canonical correlation analysis for data of mixed types.

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.

SUBMITTER: Yoon G

PROVIDER: S-EPMC8494134 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Sparse semiparametric canonical correlation analysis for data of mixed types.

Yoon Grace G Carroll Raymond J RJ Gaynanova Irina I

Biometrika 20200415 3

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 r ...[more]

PMID: 34621080

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Project description:It is now clear that major malignancies are heterogeneous diseases associated with diverse molecular properties and clinical outcomes, posing a great challenge for more individualized therapy. In the last decade, cancer molecular subtyping studies were mostly based on transcriptomic profiles, ignoring heterogeneity at other (epi-)genetic levels of gene regulation. Integrating multiple types of (epi)genomic data generates a more comprehensive landscape of biological processes, providing an opportunity to better dissect cancer heterogeneity. Here, we propose sparse canonical correlation analysis for cancer classification (SCCA-CC), which projects each type of single-omics data onto a unified space for data fusion, followed by clustering and classification analysis. Without loss of generality, as case studies, we integrated two types of omics data, mRNA and miRNA profiles, for molecular classification of ovarian cancer (n = 462), and breast cancer (n = 451). The two types of omics data were projected onto a unified space using SCCA, followed by data fusion to identify cancer subtypes. The subtypes we identified recapitulated subtypes previously recognized by other groups (all P- values < 0.001), but display more significant clinical associations. Especially in ovarian cancer, the four subtypes we identified were significantly associated with overall survival, while the taxonomy previously established by TCGA did not (P- values: 0.039 vs. 0.12). The multi-omics classifiers we established can not only classify individual types of data but also demonstrated higher accuracies on the fused data. Compared with iCluster, SCCA-CC demonstrated its superiority by identifying subtypes of higher coherence, clinical relevance, and time efficiency. In conclusion, we developed an integrated bioinformatic framework SCCA-CC for cancer molecular subtyping. Using two case studies in breast and ovarian cancer, we demonstrated its effectiveness in identifying biologically meaningful and clinically relevant subtypes. SCCA-CC presented a unique advantage in its ability to classify both single-omics data and multi-omics data, which significantly extends the applicability to various data types, and making more efficient use of published omics resources.

Dataset Information

Sparse semiparametric canonical correlation analysis for data of mixed types.

Publications

Sparse semiparametric canonical correlation analysis for data of mixed types.

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