scholarly journals Simultaneous Analysis of Multiple Data Types in Pharmacogenomic Studies Using Weighted Sparse Canonical Correlation Analysis

2012 ◽  
Vol 16 (7-8) ◽  
pp. 363-373 ◽  
Author(s):  
Prabhakar Chalise ◽  
Anthony Batzler ◽  
Ryan Abo ◽  
Liewei Wang ◽  
Brooke L. Fridley
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Simultaneous Analysis ◽  
Data Types ◽  
Multiple Data ◽  
Sparse Canonical Correlation Analysis ◽  
Pharmacogenomic Studies

scholarly journals Multi-Omics Data Fusion for Cancer Molecular Subtyping Using Sparse Canonical Correlation Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Lin Qi ◽  
Wei Wang ◽  
Tan Wu ◽  
Lina Zhu ◽  
Lingli He ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Correlation Analysis ◽  
Data Fusion ◽  
Case Studies ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Omics Data ◽  
Data Types ◽  
Molecular Subtyping ◽  
Sparse Canonical Correlation Analysis

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.

scholarly journals Multivariate association between brain function and eating disorders using sparse canonical correlation analysis

PLoS ONE ◽  
2020 ◽  
Vol 15 (8) ◽  
pp. e0237511 ◽  
Author(s):  
Hyebin Lee ◽  
Bo-yong Park ◽  
Kyoungseob Byeon ◽  
Ji Hye Won ◽  
Mansu Kim ◽  
...  
Keyword(s):  
Eating Disorders ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Brain Function ◽  
Canonical Correlation ◽  
Sparse Canonical Correlation Analysis

scholarly journals Sparse semiparametric canonical correlation analysis for data of mixed types

Biometrika ◽  
2020 ◽  
Vol 107 (3) ◽  
pp. 609-625 ◽  
Author(s):  
Grace Yoon ◽  
Raymond J Carroll ◽  
Irina Gaynanova
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Mixed Data ◽  
Gaussian Copula ◽  
Breast Cancer Patients ◽  
Data Types ◽  
Semiparametric Approach ◽  
Linear Relationships ◽  
Correlation Analysis Method

Summary Canonical correlation analysis investigates linear relationships between two sets of variables, but it often works poorly on modern datasets because of high dimensionality and mixed data types such as continuous, binary and zero-inflated. To overcome these challenges, we propose a semiparametric approach to sparse canonical correlation analysis based on the Gaussian copula. The main result of this paper is a truncated latent Gaussian copula model for data with excess zeros, which allows us to derive a rank-based estimator of the latent correlation matrix for mixed variable types without estimation of marginal transformation functions. The resulting canonical correlation analysis method works well in high-dimensional settings, as demonstrated via numerical studies, and when applied to the analysis of association between gene expression and microRNA data from breast cancer patients.

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