iSFun: an R package for integrative dimension reduction analysis.

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 multi-datasets 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: The package is available at https://CRAN.R-project.org/package=iSFun. SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.