Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/32180
Title: Statistical power of Fisher test for the detection of short periodic gene expression profiles
Authors: Liew, AWC
Law, NF 
Cao, XQ
Yan, H
Keywords: Fisher exact test
g-Statistic
Gene expression profiles
Periodicity detection
Short signal
Issue Date: 2009
Publisher: Elsevier
Source: Pattern recognition, 2009, v. 42, no. 4, p. 549-556 How to cite?
Journal: Pattern recognition 
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.
URI: http://hdl.handle.net/10397/32180
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2008.09.022
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