Statistical power of Fisher test for the detection of short periodic gene expression profiles

  • Authors:

  • Alan Wee-Chung Liew;Ngai-Fong Law;Xiao-Qin Cao;Hong Yan

  • Affiliations:

  • School of Information & Communication Technology, Gold Coast Campus, Griffith University, QLD 4222, Australia;Centre for Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong;Department of Electronic Engineering, City University of Hong Kong, Hong Kong;Department of Electronic Engineering, City University of Hong Kong, Hong Kong and School of Electronic and Information Engineering, University of Sydney, NSW 2006, Australia

  • Venue:
  • Pattern Recognition
  • Year:
  • 2009

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Abstract

Many cellular processes exhibit periodic behaviors. Hence, one of the important tasks in gene expression data analysis is to detect subset of genes that exhibit cyclicity or periodicity in their gene expression time series profiles. Unfortunately, gene expression time series profiles are usually of very short length, with very few periods, irregularly sampled and are highly contaminated with noise. This makes the detection of periodic profiles a very challenging problem. Recently, a hypothesis testing method based on the Fisher g-statistic with correction for multiple testing has been proposed to detect periodic gene expression profiles. However, it was observed that the test is not reliable if the signal length is too short. In this paper, we performed extensive simulation study to investigate the statistical power of the test as a function of noise distribution, signal length, SNR, and the false discovery rate (FDR). We have found that the number of periodic profiles can be severely underestimated for short length signal. The findings indicate that caution needs to be exercised when interpreting the test result for very short length signals.