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

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 LombScargle 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 estimat...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Alan Wee-Chung Liew, Jun Xian, Shuanhu Wu, David Keith Smith, Hong Yan
• Keywords: BMCBI 2007, Gene Expression, Gene Expression Data, Time Series