ArticlePloS one2012
Principal-oscillation-pattern analysis of gene expression.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- DREISS: Using State-Space Models to Infer the Dynamics of Gene Expression Driven by External and Internal Regulatory Networks.PLoS computational biology · 2016Article
- Cross-Disciplinary Network Comparison: Matchmaking Between Hairballs.Cell systems · 2016Article
- Linear superposition and prediction of bacterial promoter activity dynamics in complex conditions.PLoS computational biology · 2014Article
- A novel strategy for gene selection of microarray data based on gene-to-class sensitivity information.PloS one · 2014Article
- Quantifying periodicity in omics data.Frontiers in cell and developmental biology · 2014Article
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Authors and funding
4 authors.
Funding
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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.
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