iSFun: an R package for integrative dimension reduction analysis.
iSFun: an R package for integrative dimension reduction analysis.
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iSFun：用于综合降维分析的 R 包。
DOI：
10.1093/bioinformatics/btac281
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Ma,Shuangge
中科院分区：
文献类型：
--
作者：
Fang,Kuangnan;Ren,Rui;Zhang,Qingzhao;Ma,Shuangge
在高维组学数据分析中，降维技术--包括主成分分析（PCA）、偏最小二乘（PLS）和典型相关分析（CCA）--得到了广泛的应用。当有多个数据集由独立的研究与兼容的设计，综合分析已经开发出来，并显示出优于荟萃分析，其他多数据集分析，和个人数据分析。为了方便日常实践中的综合降维分析，我们开发了R软件包iSFun，它可以全面进行综合稀疏PCA，PLS和CCA，以及荟萃分析和堆叠分析。该软件包可以在同质性和异质性模型下进行分析，并使用基于幅度和符号的对比惩罚。作为一个“副产品”，本文是第一个开发基于CCA技术的综合分析，进一步扩大了综合分析的范围。可用性和实施该软件包可在https://CRAN.R-project.org/package=iSFun.Supplementary信息补充材料可在生物信息学在线。
SummaryIn 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 implementationThe package is available at https://CRAN.R-project.org/package=iSFun.Supplementary informationSupplementary materials are available atBioinformaticsonline.