Incorporating biological information in sparse principal component analysis with application to genomic data

• Type of Material: Article
• Authors: Ziyi Li, Emory University; Sandra Safo, Emory University; Qi Long, Emory University
• Language: English
• Date: 2017-07-11
• Publisher: BioMed Central
• Publication Version: Final Published Version
• Copyright Statement: © 2017 The Author(s).
• License: Creative Commons Creative Commons CC0 1.0 Universal
• Final Published Version (URL): http://dx.doi.org/10.1186/s12859-017-1740-7
• Title of Journal or Parent Work: BMC Bioinformatics
• ISSN: 1471-2105
• Volume: 18
• Issue: 1
• Start Page: 332
• End Page: 332
• Grant/Funding Information: The content is solely the responsibility of the authors and does not represent the views of the NIH.; This work was supported in part by NIH grants K12HD085850, R03CA173770, R03CA183006 and P30CA016520.
• Keywords: Structural information; Genomic data; Principal component analysis; Sparsity
• Research Categories: Biology, Genetics; Biology, Bioinformatics

Abstract

Background: Sparse principal component analysis (PCA) is a popular tool for dimensionality reduction, pattern recognition, and visualization of high dimensional data. It has been recognized that complex biological mechanisms occur through concerted relationships of multiple genes working in networks that are often represented by graphs. Recent work has shown that incorporating such biological information improves feature selection and prediction performance in regression analysis, but there has been limited work on extending this approach to PCA. In this article, we propose two new sparse PCA methods called Fused and Grouped sparse PCA that enable incorporation of prior biological information in variable selection. Results: Our simulation studies suggest that, compared to existing sparse PCA methods, the proposed methods achieve higher sensitivity and specificity when the graph structure is correctly specified, and are fairly robust to misspecified graph structures. Application to a glioblastoma gene expression dataset identified pathways that are suggested in the literature to be related with glioblastoma. Conclusions: The proposed sparse PCA methods Fused and Grouped sparse PCA can effectively incorporate prior biological information in variable selection, leading to improved feature selection and more interpretable principal component loadings and potentially providing insights on molecular underpinnings of complex diseases.