iSFun: an R package for integrative dimension reduction analysis

Abstract

Abstract Summary 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. Availability and implementation The package is available at https://CRAN.R-project.org/package=iSFun. Supplementary information Supplementary materials are available at Bioinformatics online.