Methodology article
Spectral estimation in unevenly sampled space of periodically expressed microarray time series data
-
* Corresponding author: Alan WC Liew a.liew@griffith.edu.au
1 School of Information & Communication Technology, Griffith University, Brisbane, Australia
2 Department of Electronic Engineering, City University of Hong Kong, Hong Kong
3 Department of Mathematics, Sun Yat-sen University, Guangzhou, 510275, China
4 School of Computer Science and Technology, Yantai University, Yantai, 264005, China
5 Department of Biochemistry, University of Hong Kong, Pok Fu Lam, Hong Kong
6 School of Electronic and Information Engineering, University of Sydney, NSW2006, Australia
BMC Bioinformatics 2007, 8:137 doi:10.1186/1471-2105-8-137
Published: 24 April 2007Abstract
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.