主成分分析
典型相关
R包
降维
计算机科学
数据挖掘
同质性(统计学)
偏最小二乘回归
维数(图论)
范围(计算机科学)
人工智能
机器学习
数学
计算科学
纯数学
程序设计语言
作者
Kuangnan Fang,Rui Ren,Qingzhao Zhang,Shuangge Ma
标识
DOI:10.1093/bioinformatics/btac281
摘要
In the analysis of high-dimensional omics data, dimension reduction techniques-including principal component analysis (PCA), partial least squares (PLS) and canonical correlation analysis (CCA)-have been extensively used. When there are multiple datasets generated by independent studies with compatible designs, integrative analysis has been developed and shown to outperform meta-analysis, other multidatasets analysis, and individual-data analysis. To facilitate integrative dimension reduction analysis in daily practice, we develop the R package iSFun, which can comprehensively conduct integrative sparse PCA, PLS and CCA, as well as meta-analysis and stacked analysis. The package can conduct analysis under the homogeneity and heterogeneity models and with the magnitude- and sign-based contrasted penalties. As a 'byproduct', this article is the first to develop integrative analysis built on the CCA technique, further expanding the scope of integrative analysis.The package is available at https://CRAN.R-project.org/package=iSFun.Supplementary materials are available at Bioinformatics online.
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