scholarly journals FlashPCA: fast sparse canonical correlation analysis of genomic data

2016 ◽  
Author(s):  
Gad Abraham ◽  
Michael Inouye
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Genomic Data ◽  
R Package ◽  
Rapid Analysis ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Sparse Canonical Correlation Analysis ◽  
Gene Expression Levels

SummarySparse canonical correlation analysis (SCCA) is a useful approach for correlating one set of measurements, such as single nucleotide polymorphisms (SNPs), with another set of measurements, such as gene expression levels. We present a fast implementation of SCCA, enabling rapid analysis of hundreds of thousands of SNPs together with thousands of phenotypes. Our approach is implemented both as an R package flashpcaR and within the standalone commandline tool flashpca.Availability and implementationhttps://github.com/gabraham/[email protected]

scholarly journals Identifying diagnosis-specific genotype–phenotype associations via joint multitask sparse canonical correlation analysis and classification

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i371-i379 ◽  
Author(s):  
Lei Du ◽  
Fang Liu ◽  
Kefei Liu ◽  
Xiaohui Yao ◽  
Shannon L Risacher ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Correlation Coefficients ◽  
Imaging Genetics ◽  
Supplementary Information ◽  
Local Optimum ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Sparse Canonical Correlation Analysis

Abstract Motivation Brain imaging genetics studies the complex associations between genotypic data such as single nucleotide polymorphisms (SNPs) and imaging quantitative traits (QTs). The neurodegenerative disorders usually exhibit the diversity and heterogeneity, originating from which different diagnostic groups might carry distinct imaging QTs, SNPs and their interactions. Sparse canonical correlation analysis (SCCA) is widely used to identify bi-multivariate genotype–phenotype associations. However, most existing SCCA methods are unsupervised, leading to an inability to identify diagnosis-specific genotype–phenotype associations. Results In this article, we propose a new joint multitask learning method, named MT–SCCALR, which absorbs the merits of both SCCA and logistic regression. MT–SCCALR learns genotype–phenotype associations of multiple tasks jointly, with each task focusing on identifying one diagnosis-specific genotype–phenotype pattern. Meanwhile, MT–SCCALR cannot only select relevant SNPs and imaging QTs for each diagnostic group alone, but also allows the selection of those shared by multiple diagnostic groups. We derive an efficient optimization algorithm whose convergence to a local optimum is guaranteed. Compared with two state-of-the-art methods, MT–SCCALR yields better or similar canonical correlation coefficients and classification performances. In addition, it owns much better discriminative canonical weight patterns of great interest than competitors. This demonstrates the power and capability of MTSCCAR in identifying diagnostically heterogeneous genotype–phenotype patterns, which would be helpful to understand the pathophysiology of brain disorders. Availability and implementation The software is publicly available at https://github.com/dulei323/MTSCCALR. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Group sparse canonical correlation analysis for genomic data integration

2013 ◽  
Vol 14 (1) ◽  
pp. 245 ◽  
Author(s):  
Dongdong Lin ◽  
Jigang Zhang ◽  
Jingyao Li ◽  
Vince D Calhoun ◽  
Hong-Wen Deng ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Data Integration ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Genomic Data ◽  
Genomic Data Integration ◽  
Sparse Canonical Correlation Analysis
Sign in / Sign up

Export Citation Format

Share Document