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Meta-analytic Principal Component Analysis in Integrative Omics Application
Motivation: With the prevalent usage of microarray and massively parallel sequencing, numerous high-throughput omics datasets have become available in the public domain. Integrating abundant information among omics datasets is critical to elucidate biological mechanisms. Due to the high-dimensional nature of the data, methods such as principal component analysis (PCA) have been widely applied, aiming at effective dimension reduction and exploratory visualization. Results: In this article, we combine multiple omics datasets of identical or similar biological hypothesis and introduce two variations of meta-analytic framework of PCA, namely MetaPCA. Regularization is further incorporated to facilitate sparse feature selection in MetaPCA. We apply MetaPCA and sparse MetaPCA to simulations, three transcriptomic meta-analysis studies in yeast cell cycle, prostate cancer, mouse metabolism and a TCGA pan-cancer methylation study. The result shows improved accuracy, robustness and exploratory visualization of the proposed framework. Availability and implementation: An R package MetaPCA is available online. (http://tsenglab.biostat.pitt.edu/software.htm). Contact: ctseng@pitt.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
pmid: 29186328 Bioinformatics 影响因子: 4.531 发表日期: 20180415 官网 免费下载 全文下载
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