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. 2022 May 26;38(11):3134-3135.
doi: 10.1093/bioinformatics/btac281.

iSFun: an R package for integrative dimension reduction analysis

Kuangnan Fang  1 , Rui Ren  1 , Qingzhao Zhang  1   2 , Shuangge Ma  3
Affiliations

iSFun: an R package for integrative dimension reduction analysis

Kuangnan Fang et al. Bioinformatics. .

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.

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Figures

Fig. 1.
Fig. 1.
Workflow of the iSFun package
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