Identifying Gene Expression Patterns in Mice
Author Information
Author(s): Li Hao, Wood Constance L, Liu Yushu, Getchell Thomas V, Getchell Marilyn L, Stromberg Arnold J
Primary Institution: University of Kentucky
Hypothesis
Can planned linear contrasts effectively analyze time-course microarray experiments to identify gene expression patterns?
Conclusion
The study successfully categorized 1234 genes into four classes based on their initial significant changes in expression over time.
Supporting Evidence
- 1234 genes were identified as significant based on the FDR control.
- The largest P-value considered significant was 0.009545.
- Genes were categorized into four classes based on the timing of their initial expression changes.
Takeaway
Researchers looked at how genes change over time after a specific event in mice, finding that some genes change quickly while others take longer.
Methodology
ANOVA F test and planned linear contrasts were used to analyze gene expression data from microarray experiments.
Potential Biases
Potential biases may arise from small sample sizes and the exploratory nature of microarray experiments.
Limitations
The study may not account for the exact magnitudes of gene expression changes and assumes uniform sampling intervals.
Participant Demographics
The study involved 3 male mice per time point.
Statistical Information
P-Value
0.009545
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website