I see your points. but the strange thing is that actually

> sam.out1
   Delta    p0 False Called     FDR
5  2.01 0.519     0    159       0
> sam.out2
    Delta p0 False Called     FDR
5  2.01 0       1     161         0

do you see? same parameter of delta, but an higer number of significant
genes fcor higer FC. this make no sense to me! we are not speaking about
FDR. but of crude results from the test!

On Dec 12, 2007 5:15 PM, Holger Schwender <holger.schw@gmx.de> wrote:

> Hi John,
>
> I am not sure, but this might be due to the fact that in siggenes the fold
> change is used to filter out genes prior to the actual SAM analysis. Thus,
> only the permuted values of the test statistics for the remaining genes,
> i.e. genes with a fold change larger than R.fold (or smaller than
> 1/R.fold), are used to estimate the null distribution and to compute d.bar,
> i.e. the values of the test statistic expected under the null, instead of
> using the permuted values of all genes. This might lead to these strange
> results.
>
> Best,
> Holger
>
>
>
>
> -------- Original-Nachricht --------
> > Datum: Tue, 11 Dec 2007 19:20:25 +0100
> > Von: "John Lande" <john.lande77@gmail.com>
> > An: bioconductor@stat.math.ethz.ch
> > Betreff: [BioC] siggenes fc threshold
>
> > dear biocoductors,
> >
> > I want to use siggenes, and sam to find differentially regulated genes,
> > but
> > I have problems with siggenes function, and possibly didn't understand
> > properly something.
> > here I will report an example that emulate the problem:
> >
> > library(siggenes)
> > data(golub)
> > sam.out1 <- sam(golub, golub.cl, rand = 123, gene.names =
> > golub.gnames[,3],
> > med=TRUE, lambda=.5,method=d.stat, B=5, R.fold=1, delta=seq(0.01, 3, 0.5
> ))
> > sam.out2 <- sam(golub, golub.cl, rand = 123, gene.names =
> > golub.gnames[,3],
> > med=TRUE, lambda=.5,method=d.stat, B=5, R.fold=2, delta=seq(0.01, 3, 0.5
> ))
> >
> > I use the parameter R.fold to set the minimum FC I want for my list of
> > significant genes.
> > the problem is this: when I launch
> >
> > > sam.out1
> > SAM Analysis for the Two-Class Unpaired Case Assuming Unequal Variances
> >
> >   Delta    p0 False Called     FDR
> > 1  0.01 0.519  2950   3007 0.50933
> > 2  0.51 0.519   478   1638 0.15151
> > 3  1.01 0.519    38    839 0.02351
> > 4  1.51 0.519     1    380 0.00137
> > 5  2.01 0.519     0    159       0
> > 6  2.51 0.519     0     74       0
> >
> > > sam.out2
> > SAM Analysis for the Two-Class Unpaired Case Assuming Unequal Variances
> >
> >   Delta p0 False Called FDR
> > 1  0.01  0    17    166   0
> > 2  0.51  0    17    166   0
> > 3  1.01  0    12    164   0
> > 4  1.51  0     3    163   0
> > 5  2.01  0     1    161   0
> > 6  2.51  0     0    155   0
> >
> > you can see that the sam with higher FC with a delta of 2.51 has an
> higher
> > number of significant genes than the one with 1. to me does not make
> much
> > sense.
> > by the way I also tried to use sam in excel and I don't have the same
> > problems. furthermore the dynamic range of delta is much lower. do you
> > have
> > any idea?
> >
> > what do I do wrong?
> >
> > best regards
> >
> >       [[alternative HTML version deleted]]
> >
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