The globaltest is certainly applicable to small groups. The permutationtest is automatically chosen for small samplesizes because in that case the normality of the distribution of the expression values for each gene can not be reliably estimated/assumed. 
The permutation p-values you are seeing (0.005) mean that of all possible permuations of samples over 2 groups of 5, your combination of treated and control subjects shows the most significant difference.
 
 
You can use the function gt.multtest() on your results of all 170 kegg pathways, and then choose a suitable threshold for the p-value. (see also the vignette a few paragraphs below the KEGG example
 
Jan
 
 

________________________________

Van: mike Ad. [mailto:mikeaddr@hotmail.com]
Verzonden: di 12-sep-06 9:57
Aan: Oosting, J. (PATH); bioconductor@stat.math.ethz.ch
Onderwerp: RE: [BioC] globaltest question



Hi,
Thanks for the reply!
I have two following questions:
1. It is ok to use globaltest for small groups? (totally 10 arrays for 2
groups in my case.)
2. Different method ("auto", "asymptotic"...) in the globaltest gives
different p-values, how should one set threshold to pick out the significant
pathways?
Thanks,
/Mike




>From: "Oosting, J. (PATH)" <J.Oosting@lumc.nl>
>To: "mike Ad." <mikeaddr@hotmail.com>,<bioconductor@stat.math.ethz.ch>
>Subject: RE: [BioC] globaltest question
>Date: Tue, 12 Sep 2006 16:42:25 +0200
>
>On small groups the globaltest automatically uses permutation tests. You
>can see in your result that in this case there are 210 permutations, and
>all p-values will therefore be multiples of 1/210, with a minimum of
>1/210(=0.0047619). You can force it to use a more gliding scale by using
>the argument method="asymptotic".
>
>Jan
>
>-----Original Message-----
>From: bioconductor-bounces@stat.math.ethz.ch
>[mailto:bioconductor-bounces@stat.math.ethz.ch] On Behalf Of mike Ad.
>Sent: dinsdag 12 september 2006 16:23
>To: bioconductor@stat.math.ethz.ch
>Subject: [BioC] globaltest question
>
>Dear list,
>
>I am new to use the "globaltest" packages (version "4.2.0"). I have 10
>mouse arrays from two groups (control and treated). I tested them
>against all the kegg pathways. The result looks stage to me because
>among the 171 pathways tested, most of them have the identical p-value.
>And that p-value is the smallest.
>The code I used is listed, could someone help to tell me where went
>wrong with my code?
>
>Thanks!
>
>/Mike
>
>kegg<-as.list(mouse4302PATH2PROBE)
>
>gtkegg<-globaltest(affy_expression, diagno, kegg) ##where the first
>argument "affy_expression" is the affy expression data set I got by
>using function "exprs()", each row is one affy probe and each column is
>from one array.
>## the second argument "diagno" is a vector containing 10 group names
>("treated" or "control") for the 10 arrays and they are in the
>corresponding order to the 10 columns in the expression data.
>
>gtkegg<-sort(gtkegg)
>
>#Just list the top 5 of the result, the P-value are identical, what's
>wrong?
>gtkegg[1:5]
>Global Test result:
>Data: 10 samples with 45101 genes; 5 pathways tested
>Model: logistic
>Method: All 210 permutations
>
>       Genes Tested Statistic Q Expected Q sd of Q   P-value
>00623    12     12      37.552     9.1318  9.2135 0.0047619
>00440    47     47      13.010     3.3143  1.7585 0.0047619
>00624    43     43      57.812     9.1819  8.2350 0.0047619
>00625    19     19      71.404    12.6820 10.4620 0.0047619
>00626    28     28      15.648     3.5587  2.2039 0.0047619
>
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