Spectral estimation in unevenly sampled space of periodically expressed microarray time series data

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Title Spectral estimation in unevenly sampled space of periodically expressed microarray time series data
Author Liew, Alan Wee-Chung; Xian, Jun; Wu, Shuanhu; Smith, David; Yan, Hong
Journal Name BMC Bioinformatics
Year Published 2007
Place of publication United Kingdom
Publisher BioMed Central
Abstract Background: Periodogram analysis of time-series is widespread in biology. A new challenge for analyzing the microarray time series data is to identify genes that are periodically expressed. Such challenge occurs due to the fact that the observed time series usually exhibit non-idealities, such as noise, short length, and unevenly sampled time points. Most methods used in the literature operate on evenly sampled time series and are not suitable for unevenly sampled time series. Results: For evenly sampled data, methods based on the classical Fourier periodogram are often used to detect periodically expressed gene. Recently, the Lomb-Scargle algorithm has been applied to unevenly sampled gene expression data for spectral estimation. However, since the Lomb-Scargle method assumes that there is a single stationary sinusoid wave with infinite support, it introduces spurious periodic components in the periodogram for data with a finite length. In this paper, we propose a new spectral estimation algorithm for unevenly sampled gene expression data. The new method is based on signal reconstruction in a shift-invariant signal space, where a direct spectral estimation procedure is developed using the B-spline basis. Experiments on simulated noisy gene expression profiles show that our algorithm is superior to the Lomb-Scargle algorithm and the classical Fourier periodogram based method in detecting periodically expressed genes. We have applied our algorithm to the Plasmodium falciparum and Yeast gene expression data and the results show that the algorithm is able to detect biologically meaningful periodically expressed genes. Conclusion: We have proposed an effective method for identifying periodic genes in unevenly sampled space of microarray time series gene expression data. The method can also be used as an effective tool for gene expression time series interpolation or resampling.
Peer Reviewed Yes
Published Yes
Publisher URI http://www.biomedcentral.com/
Alternative URI http://dx.doi.org/10.1186/1471-2105-8-137
Copyright Statement Copyright 2007 Liew et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Volume 8
Page from 1
Page to 19
ISSN 1471-2105
Date Accessioned 2007-06-13
Date Available 2009-10-16T05:19:57Z
Language en_AU
Comments Page numbers are not for citation purposes. Instead, this article has the unique article number of 137.
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject PRE2009-Gene Expression; PRE2009-Signal Processing
URI http://hdl.handle.net/10072/17077
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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