Year: 2022
Type: article
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.
Source: Bioinformatics
Authors: Kuangnan Fang, Rui Ren, Qingzhao Zhang, Shuangge Ma
Institutions: Xiamen University, Yale University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: bronze
Awards: G1354162025, Yale SPORE in Lung Cancer (YSILC): The Biology and Personalized Treatment of Lung Cancer, G3055610919, G3089755109, Novel Methods for Identifying Genetic Interactions for Cancer Prognosis, Integrated Cancer Modeling: A New Dimension, G734671176, G8490343050, G9000144101, G9000169908, G9000286398
OpenAlex ID: W4224219720