Entering edit mode
Are repeated measures anova with limma(IE using duplicate correlation and a blocking variable and a measurement covariate) directional? Will it automatically test for hypothesis where it is expected that the measurement variables are negatively correlated? Or does it only test the hypothesis that means of subsequent measurements are not the same? Is there anyway to do the former without repeating the analysis with the signs flipped on one of the measurements(with two measurements).
Confusion is contagious :)
I've been thinking about it some, and maybe this will get a better answer.
Suppose instead of a design matrix like such:
~Group+Measures
I did
~Group:Measures
Then I could test various contrasts between the combinations of measures and groups
such as
makeContrasts(GroupA:MeasureOne+GroupA:MeasureTwo+GroupB:MeasureOne-GroupB:MeasureTwo,design)
Now what I wonder is does duplicateCorrelation use these designs of the ~Group:Measure variety?
Also, what is my null hypothesis when my alternate hypothesis is
GroupA:MeasureOne is not correlated or positively correlated with GroupA:MesureTwo but GroupB:MeasureOne is negative correlated with GroupB:MeasureTwo?
duplicateCorrelationwill use whateverdesignmatrix you supply to it. You must decide whether the interaction model (i.e.,~Group*Measures, or something equivalent with the:operator) is more appropriate than the additive model (i.e.,~Group+Measures) for your circumstances. If so, then you should use the former throughout your entire analysis. In fact, you could make things a lot simpler by using a one-way layout with the combinations:As for your second question, I'm not sure what you're trying to do. Why do you want to deal with correlations between coefficients? For each gene, you've only got one estimate of each coefficient, so it'd be like trying to figure out the correlation from a single point. Perhaps you could give this a bit more biological context, so we know the relevant question that you're trying to answer.