Question: siggenes parameters

0

Assa Yeroslaviz •

**1.4k**wrote:Hi Holger,
Thanks for the response (I am sorry to answer so late as I was on a
business
trip).
I will look into the description to try and understand it better.
I already thought that the number is too low. I am still not sure
though
about the first two runs.
What about the R.fold parameter?
Does it make sense to set it before I run the SAM analysis, or is it
better
to first run it with all the genes and than exclude those genes with a
fold
induction under a certain threshold?
In the second run I get almost the same amount of called genes with a
"better" FDR value.
Is there a way to say which FDR value can be accepted?
Thanks
Assa
On Tue, Jun 28, 2011 at 13:11, Holger Schwender <holger.schw@gmx.de>
wrote:
> Hi Assa,
>
> siggenes is described in
>
> http://cran.r-project.org/doc/Rnews/Rnews_2006-5.pdf
>
> on pages 45ff, and with a bit more technical details in
>
> https://eldorado.tu-
dortmund.de/bitstream/2003/23306/1/diss_schwender.pdf
>
> Section 6.3.2. Another possibility is to look into the code for sam,
in
> particular into the functions d.stat and siggenes:::stats.cal.
>
> The reason why the FDR is so low in the third example is the very,
very low
> estimate for p0. Take a look at the number of falsely called genes
(False)
> for about the same number of called genes. These numbers are close
to each
> other. The FDR is estimated by
>
> p0 * False/Called
>
> so the FDR gets very low if p0 is very small.
>
> I would thus not trust the results of the third analysis, as the p0
> estimation has not worked properly here. Not really sure why, but
the reason
> might be that too many null genes are removed when use.dm=FALSE.
>
> Best,
> Holger
>
> -------- Original-Nachricht --------
> > Datum: Tue, 28 Jun 2011 09:41:53 +0200
> > Von: Assa Yeroslaviz <frymor@gmail.com>
> > An: bioconductor <bioconductor@stat.math.ethz.ch>,
holger.schw@gmx.de
> > Betreff: siggenes parameters
>
> > Hi everybody,
> >
> > I have a question about the behavior of siggenes in R (2.12) with
> > different
> > parameters.
> > Maybe I need to understand better how siggenes calculated the
> > differentially
> > regulated genes, but I would like to ask you for your help in
> > understanding
> > it.
> >
> > Maybe you can also direct me to where I can find the answers for
this
> > problem(s)
> >
> > I am running a sam analysis of wild type vs. mutant in drosophila
miRNA
> > microarrays.
> >
> > I first read the files into an affybatch and than normalized them
(RMA).
> > After normalizing the arrays I extracted all non-drosophila miRNA
from my
> > expressionSet and ran the sam analysis only with the 186 dme-miRNA
> probes.
> >
> > cl <- c(0,0,0,1,1,1) # "wt1", "wt2", "wt3", "mt1", "mt2", "mt3"
> > sam.out <- sam(dme_rma,cl, var.equal=FALSE, B=100,
include.zero=FALSE,
> > gene.names=Probe_names, na.replace=FALSE, rand=123)
> >
> > I run the sam analysis with different parameters and got *very*
different
> > results.
> >
> > the first run was with the default parameters. Here I got these
results:
> > SAM Analysis for the Two-Class Unpaired Case Assuming Unequal
Variances
> >
> > s0 = 0.3176 (The 55 % quantile of the s values.)
> >
> > Delta p0 False Called FDR cutlow cutup j2 j1
> > 1 0.10 0.882 74.85 92 0.717 -0.130 2.135 91 186
> > 2 0.11 0.882 74.85 92 0.717 -0.130 2.135 91 186
> > 3 0.12 0.882 28.25 39 0.639 -0.575 2.135 38 186
> > 4 0.13 0.882 24.35 38 0.565 -0.633 2.135 37 186
> > ...
> > 10 0.19 0.882 21.65 37 0.516 -0.672 2.135 36 186
> > 11 0.20 0.882 12.4 24 0.456 -0.860 2.135 23 186
> > 12 0.21 0.882 12.4 24 0.456 -0.860 2.135 23 186
> > 13 0.22 0.882 12.4 24 0.456 -0.860 2.135 23 186
> > 14 0.23 0.882 10.9 22 0.437 -0.905 2.135 21 186
> > 15 0.24 0.882 0.4 1 0.353 -Inf 2.135 0 186
> > ...
> > 20 0.29 0.882 0.4 1 0.353 -Inf 2.135 0 186
> > 21 0.30 0.882 0 0 0 -Inf Inf 0 187
> > 22 0.31 0.882 0 0 0 -Inf Inf 0 187
> >
> > I get here a very high FDR value for very few miRNAs.
> >
> > For my second run I added the parameter R.fold = 1.2, to exclude
all
> > miRNAs
> > under this value from the analysis.
> > Here I get much better results with lower FDR values:
> >
> > Number of variables having a fold change >= 1.2 or <= 0.8333 : 90
> >
> > s0 = 0.0477 (The 0 % quantile of the s values.)
> >
> > Delta p0 False Called FDR cutlow cutup j2 j1
> > 1 0.1 0.378 55.5 82 0.2557 -0.708 0.425 45 54
> > 2 0.2 0.378 52.25 80 0.2467 -0.708 0.562 45 56
> > 3 0.3 0.378 48.4 79 0.2314 -0.708 0.676 45 57
> > 4 0.5 0.378 24.9 48 0.1960 -0.708 2.504 45 88
> > 5 0.6 0.378 23.7 45 0.1990 -0.708 Inf 45 91
> > 6 0.7 0.378 10.65 31 0.1298 -1.170 Inf 31 91
> > 7 0.8 0.378 6.75 25 0.1020 -1.513 Inf 25 91
> > 8 1.0 0.378 0.85 4 0.0803 -2.712 Inf 4 91
> > 9 1.1 0.378 0.25 1 0.0944 -4.810 Inf 1 91
> > 10 1.2 0.378 0 0 0 -Inf Inf 0 91
> >
> > I than ran a third sam with the parameter use.dm=FALSE. This gave
me
> again
> > different results with much lower FDR values:
> >
> > Number of variables having a fold change >= 1.2 or <= 0.8333 : 97
> >
> > s0 = 0.1488 (The 5 % quantile of the s values.)
> >
> > Delta p0 False Called FDR cutlow cutup j2 j1
> > 1 0.1 0.009 53.5 81 0.00612 -0.541 0.545 48 65
> > 2 0.2 0.009 50.65 80 0.00587 -0.541 0.616 48 66
> > 3 0.3 0.009 26.85 48 0.00519 -0.541 Inf 48 98
> > 4 0.4 0.009 26.85 48 0.00519 -0.541 Inf 48 98
> > 5 0.5 0.009 26.85 48 0.00519 -0.541 Inf 48 98
> > 6 0.6 0.009 0 0 0 -Inf Inf 0 98
> > 7 0.7 0.009 0 0 0 -Inf Inf 0 98
> > 8 0.8 0.009 0 0 0 -Inf Inf 0 98
> > 9 0.9 0.009 0 0 0 -Inf Inf 0 98
> > 10 1.0 0.009 0 0 0 -Inf Inf 0 98
> >
> >
> > As you can see my obvious problem. Which is the better (right???)
way of
> > running this analysis.
> > I think, to understand that, one needs to know how SAM works. I
don't
> > understand why I get such different results in the FDR between the
three
> > possibilities.
> >
> > As far as I understand it, the lower the FDR value, the better.
But how
> > can
> > it be, that I have in the third run over 80 miRNAs with such a low
FDR,
> > while I have only few DE miRNAs in the first run with a much
higher FDR
> > value?
> >
> > I would appreciate any help,
> >
> > thanks
> > Assa
>
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