Hello,

 

I am not sure whether an alike question has been posted previously, but
the searchable archives are only searchable until July 18 2005. So I
might have missed some postings.

 

I have used Limma to calculate the ordinary t-statistics as is written
down in the Limma tutorial:

 

design=cbind(Control=1,
Factor_vs_Control=as.factor(pData(x.WT)$treatment))

design

 

     Control Factor_vs_Control

[1,]       1             1

[2,]       1             1

[3,]       1             1

[4,]       1             2

[5,]       1             2

[6,]       1             2

 

 

Y <- as.matrix(Intensities)

fit <- lmFit(Y, design)

 

# Calculate the ordinary t-statistics

tstat.ord <- fit$coef / fit$stdev.unscaled / fit$sigma

ordinary.p.values <- 2*(1-pt(abs(tstat.ord), df=fit$df.residual))

 

When I compare the obtained p-values with the "pairwise.comparison"
function in the package "simpleaffy", I find differences which I could
trace back to the assumption whether or not both groups have equal
variances. Limma supposes that the groups have equal variances, whereas
simpleaffy does not (on which I agree).

 

My question is: where does this assumption come from? Did I do something
wrong in my design matrix or is Limma simply assuming equal variances?
And if the latter is true, can I use Limma in the case that unequal
variances are required?

 

Regards,

 

Dr. Philip de Groot

Wageningen University


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