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. Conclusions: The C&G procedure proposed is an effective method for identifying statistically significant periodic genes in microarray time series gene expression data.
| Original language | English (US) |
|---|---|
| Article number | 286 |
| Journal | BMC Bioinformatics |
| Volume | 6 |
| DOIs | |
| State | Published - Nov 30 2005 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Medicine(all)
- Structural Biology
- Applied Mathematics
Cite this
Identification of significant periodic genes in microarray gene expression data. / Chen, Jie.
In: BMC Bioinformatics, Vol. 6, 286, 30.11.2005.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Identification of significant periodic genes in microarray gene expression data
AU - Chen, Jie
PY - 2005/11/30
Y1 - 2005/11/30
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=29144484744&partnerID=8YFLogxK
U2 - 10.1186/1471-2105-6-286
DO - 10.1186/1471-2105-6-286
M3 - Article
VL - 6
JO - BMC Bioinformatics
JF - BMC Bioinformatics
SN - 1471-2105
M1 - 286
ER -