Hi James
thanks for the answer, :-) Perl is not so bad

With my data, I get this....

colnames(siggenes.table$genes.lo)
[1] "Row"               "Gene ID"           "Gene Name"
"Score(d)"
[5] "Numerator(r)"      "Denominator(s+s0)" "q-value(%)"

Did I run SAMR in the wrong manner?




On Tue, Sep 14, 2010 at 2:36 PM, James W. MacDonald
<jmacdon@med.umich.edu>wrote:

> Hi Brian,
>
>
> On 9/14/2010 4:30 AM, brian snail wrote:
>
>> Hi.
>> Below is an outline of the code I am using to analyse two colour array
>> data.
>> Normal = Cy3
>> Case = Cy5
>> There are 3 replicates.
>>
>> Can the fold change be reported back in the siggenes.table? From the
>> documentation, it is not obvious how this is done.
>>
>
> I'm not sure what you are asking here. If I run the example for
> samr.compute.siggenes.table, and then look at the colnames for the two
> relevant list members I get:
>
> > colnames(siggenes.table$genes.lo)
> [1] "Row"               "Gene ID"           "Gene Name"
> [4] "Score(d)"          "Numerator(r)"      "Denominator(s+s0)"
> [7] "Fold Change"       "q-value(%)"
> > colnames(siggenes.table$genes.up)
> [1] "Row"               "Gene ID"           "Gene Name"
> [4] "Score(d)"          "Numerator(r)"      "Denominator(s+s0)"
> [7] "Fold Change"       "q-value(%)"
>
> But maybe I misunderstand your question?
>
>
>
>> Thank you for any help.
>>
>>
>>
>>
>>
>> # Code for Two colour data normalised with maNorm. ########
>>
>> data.normalised<- (two colour array matrix of data);
>>
>> # Make column names 1, 2 and 3
>> colnames(data.normalised)<- seq(1:3);
>>
>
> Ugh. This isn't Perl, or god forbid, SAS. Although the R interpreter is
> happy to strip off these trailing semicolons, they are not necessary, and
> IMO really ugly.
>
> Best,
>
> Jim
>
>
>
>
>> # Make the row names gene names
>> rownames(data.normalised)<- gene_names;
>>
>> library(impute);
>> # Replace nas with numbers
>> imputed<-impute.knn(data.normalised,k=3);
>>
>> # Take the raw data from impute object
>> x<-imputed$data;
>>
>> # One class, so a list of 1's
>> y<-c(rep(1,3));
>>
>> # Prepare the samr data
>> data.sam<- list(x=x, y=y, genenames=rownames(data.normalised),
>> geneid=gene_names,logged2=T);
>>
>> library(samr);
>>
>> # Do the one class SAMR analysis
>> samr.obj<- samr(data.sam,resp.type="One class",nperms=100);
>>
>> # Set up a delta value
>> delta=0.4
>>
>> # Compute the delta table
>> delta.table<- samr.compute.delta.table(samr.obj)
>>
>> # Collect the significant genes into a table
>> siggenes.table<-samr.compute.siggenes.table(samr.obj,delta, data.sam,
>> delta.table);
>>
>>        [[alternative HTML version deleted]]
>>
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>
> --
> James W. MacDonald, M.S.
> Biostatistician
> Douglas Lab
> University of Michigan
> Department of Human Genetics
> 5912 Buhl
> 1241 E. Catherine St.
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