Identification of significant periodic genes in microarray gene expression data.
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.


CONCLUSIONS
The C&G procedure proposed is an effective method for identifying statistically significant periodic genes in microarray time series gene expression data.
Chen
Jie
J
Department of Mathematics and Statistics, University of Missouri-Kansas City, 5100 Rockhill Road, Kansas City, MO, USA. chenj@umkc.edu
D000465:Algorithms; D002453:Cell Cycle; D045744:Cell Line, Tumor; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D005189:False Positive Reactions; D005347:Fibroblasts / Q000378:metabolism; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D005798:Genes, Bacterial; D005800:Genes, Fungal; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D013997:Time Factors