ISFun: an R package for integrative dimension reduction analysis.

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

SUBMITTER: Fang K 

PROVIDER: S-EPMC9154261 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature