[BioC] globaltest question

[mike Ad.]

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 at lumc.nl>
>To: "mike Ad." <mikeaddr at hotmail.com>,<bioconductor at 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 at stat.math.ethz.ch
>[mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of mike Ad.
>Sent: dinsdag 12 september 2006 16:23
>To: bioconductor at 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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