Principal-oscillation-pattern analysis of gene expression

5Citations
Citations of this article
38Readers
Mendeley users who have this article in their library.

Abstract

Principal-oscillation-pattern (POP) analysis is a multivariate and systematic technique for identifying the dynamic characteristics of a system from time-series data. In this study, we demonstrate the first application of POP analysis to genome-wide time-series gene-expression data. We use POP analysis to infer oscillation patterns in gene expression. Typically, a genomic system matrix cannot be directly estimated because the number of genes is usually much larger than the number of time points in a genomic study. Thus, we first identify the POPs of the eigen-genomic system that consists of the first few significant eigengenes obtained by singular value decomposition. By using the linear relationship between eigengenes and genes, we then infer the POPs of the genes. Both simulation data and real-world data are used in this study to demonstrate the applicability of POP analysis to genomic data. We show that POP analysis not only compares favorably with experiments and existing computational methods, but that it also provides complementary information relative to other approaches. © 2012 Wang et al.

Cite

CITATION STYLE

APA

Wang, D., Arapostathis, A., Wilke, C. O., & Markey, M. K. (2012). Principal-oscillation-pattern analysis of gene expression. PLoS ONE, 7(1). https://doi.org/10.1371/journal.pone.0028805

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free