Analyzing Incomplete Time-Course Data in Gene Studies
Author Information
Author(s): Tan Qihua, Thomassen Mads, Hjelmborg Jacob v. B., Clemmensen Anders, Andersen Klaus Ejner, Petersen Thomas K., McGue Matthew, Christensen Kaare, Kruse Torben A.
Primary Institution: Odense University Hospital
Hypothesis
Can a growth curve model with fractional polynomials effectively analyze incomplete time-course data in microarray gene expression studies?
Conclusion
The growth curve model with fractional polynomials can efficiently handle missing observations and identify significant time-dependent gene expression patterns.
Supporting Evidence
- The model identified significant time-course expression patterns despite missing data.
- Fifteen genes showed significant changes in expression over time.
- The method allows for flexible modeling of gene expression trajectories.
Takeaway
This study shows a way to analyze gene data over time, even when some information is missing, helping scientists understand how genes react to treatments.
Methodology
The study used a growth curve model with fractional polynomials to analyze gene expression data collected at multiple time points, accounting for missing observations.
Limitations
The model's effectiveness may be limited by the number of time points available and the proportion of missing data.
Participant Demographics
9 human volunteers were studied, with biopsies taken at various time points after exposure to a chemical irritant.
Statistical Information
P-Value
0.030
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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