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iSFun: an R package for integrative dimension reduction analysis.

Kuangnan Fang1, Rui Ren1, Qingzhao Zhang1,2

  • 1Department of Statistics and Data Science, Xiamen University, Xiamen 361005, China.

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The R package iSFun enables integrative dimension reduction analysis for omics data, including sparse Principal Component Analysis (PCA), Partial Least Squares (PLS), and Canonical Correlation Analysis (CCA). It facilitates multi-dataset analysis, outperforming traditional methods.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • High-dimensional omics data analysis commonly employs dimension reduction techniques like PCA, PLS, and CCA.
  • Integrative analysis of multiple independent omics datasets can yield superior results compared to meta-analysis or individual-data analysis.

Purpose of the Study:

  • To develop an R package, iSFun, for practical integrative dimension reduction analysis.
  • To offer a comprehensive tool for sparse PCA, PLS, and CCA across multiple datasets.
  • To introduce the first integrative analysis method based on Canonical Correlation Analysis (CCA).

Main Methods:

  • The iSFun package implements integrative sparse PCA, PLS, and CCA.
  • It supports both homogeneity and heterogeneity models for analysis.
  • Contrasted penalties based on magnitude and sign are incorporated.

Main Results:

  • iSFun provides a unified framework for various integrative dimension reduction techniques.
  • The package facilitates meta-analysis and stacked analysis alongside integrative methods.
  • This work extends integrative analysis by incorporating CCA.

Conclusions:

  • The iSFun package simplifies and enhances integrative dimension reduction analysis for omics data.
  • It offers a versatile and powerful tool for researchers working with multiple datasets.
  • The development expands the methodological landscape of integrative omics data analysis.