Parametric spectral analysis of malaria gene expression time series data

Authors:
Liping Du;Shuanhu Wu;Alan Wee-Chung Liew;David Keith Smith;Hong Yan
Affiliations:
Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong;Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong;Department of Computer Science and Engineering, Chinese University of Hong Kong, Shatin, Hong Kong;Department of Biochemistry, University of Hong Kong, Pok Fu Lam, Hong Kong;Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong
Venue:
CompLife'06 Proceedings of the Second international conference on Computational Life Sciences
Year:
2006

Citing 4
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Spectral analysis of microarray gene expression time series data of Plasmodium falciparum

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Abstract

Spectral analysis of DNA microarray gene expressions time series data is important for understanding the regulation of gene expression and gene function of the Plasmodium falciparum in the intraerythrocytic developmental cycle. In this paper, we propose a new strategy to analyze the cell cycle regulation of gene expression profiles based on the combination of singular spectrum analysis (SSA) and autoregressive (AR) spectral estimation. Using the SSA, we extract the dominant trend of data and reduce the effect of noise. Based on the AR analysis, high resolution spectra can be produced. Experiment results show that our method can extract more genes and the information can be useful for new drug design.