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. 2005 Nov 30:6:286.
doi: 10.1186/1471-2105-6-286.

Identification of significant periodic genes in microarray gene expression data

Jie Chen  1
Affiliations

Identification of significant periodic genes in microarray gene expression data

Jie Chen. BMC Bioinformatics. .

Abstract

Background: One frequent application of microarray experiments is in the study of monitoring gene activities in a cell during cell cycle or cell division. A new challenge for analyzing the microarray experiments is to identify genes that are statistically significantly periodically expressed during the cell cycle. Such a challenge occurs due to the large number of genes that are simultaneously measured, a moderate to small number of measurements per gene taken at different time points, and high levels of non-normal random noises inherited in the data.

Results: Based on two statistical hypothesis testing methods for identifying periodic time series, a novel statistical inference approach, the C&G procedure, is proposed to effectively screen out statistically significantly periodically expressed genes. The approach is then applied to yeast and bacterial cell cycle gene expression data sets, as well as to human fibroblasts and human cancer cell line data sets, and significantly periodically expressed genes are successfully identified.

Conclusion: The C&G procedure proposed is an effective method for identifying statistically significant periodic genes in microarray time series gene expression data.

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Figures

Figure 1
Figure 1
The gene (ORF00082) in Laub data that is not considered periodic in this paper.
Figure 2
Figure 2
The nine most significant periodic genes in Elution data.
Figure 3
Figure 3
The nine most significant genes in set D for Elution data.
Figure 4
Figure 4
The two genes in set D of N3 data.
Figure 5
Figure 5
The six significant periodic genes in set K of Score1 data.
See this image and copyright information in PMC

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