Analysis%20of%20time-course%20microarray%20data%20using%20the%20ANCOVA%20framework - PowerPoint PPT Presentation

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Analysis%20of%20time-course%20microarray%20data%20using%20the%20ANCOVA%20framework

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Title: Analysis%20of%20time-course%20microarray%20data%20using%20the%20ANCOVA%20framework


1
Analysis of time-course microarray data using the
ANCOVA framework
  • Tzu L. Phang and Katherina Kechris
  • University of Colorado Health Sciences Center

2
Microarray Time Course Experimental Design and
Analysis
3
Downsides of the previous approach
1. ANCOVA violates the homogeneity of regression
slope assumption
2. Contrast comparisons compared to a baseline
factor, making non-baseline comparisons
non-trivial
3. Only one categorical factor is allowed
4. No convenient tool for biologists to select
comparison of interest
4
Proposed Analysis Flow
Model(Treatment Strains Time Time2
Interaction)
5
Results
6
Conclusion
  • We have established a comprehensive statistical
    framework to study the effect of different
    variables for multiple-factors microarray time
    course data.
  • Each of these effects categorical, continuous,
    and interaction, should be handled with
    appropriate methods (Tukey HSD, Johnson-Neyman,
    etc)
  • Future challenges
  • Build a user friendly tool to select genes of
    interest amid the sea of interacting effects.
  • Establish the relationship between these effects
    across genes and experiments using network
    graphing technology.
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