Statistics and Its Interface

Volume 3 (2010)

Number 3

Periodicity analysis of DNA microarray gene expression time series profiles in mouse segmentation clock data

Pages: 413 – 418

DOI: https://dx.doi.org/10.4310/SII.2010.v3.n3.a13

Authors

Alan Wee-Chung Liew (School of Information & Communication Technology, Griffith University, Gold Coast, Australia)

Vivian Tsz-Yan Tang (Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong)

Hong Yan (Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong)

Abstract

With microarray technology, gene expression profiles are produced at a rapid rate. It remains a challenge for biologists to robustly identify periodic gene expression profiles when the time series have short data length and contain a high level of noise. An effective method is proposed in this paper to analyze the periodicity of gene expression time series using singular value decomposition (SVD), singular spectrum analysis (SSA) and autoregressive (AR) model-based spectral estimation. Using these procedures, noise can be filtered out and over 85% of periodic gene expression can be identified in the mouse segmentation clock data set.

Keywords

singular value decomposition (SVD), singular spectrum analysis (SSA), segmentation clock, periodicity analysis, microarray time series analysis

2010 Mathematics Subject Classification

60K35

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Published 1 January 2010