[BioC] How to save result from limma

[Cei Abreu-Goodger]

In many cases, I tend to prefer the topTable output if only for the 
reason that the "ID" column comes first (I know, a very poor reason). Is 
there any strong reason why we should never use topTable for full 
output? I thought that, at least for single contrasts, the information 
is the same than what can be obtained by write.fit. Also, having a 
data.frame object helps manipulation prior to saving as a file.

Cei

James W. MacDonald wrote:
> Actually, write.fit() is what you are looking for. From ?topTable:
>
> Note:
>
>      This is not the right function to use to create summary statistics
>      for all the probes on an array. Please consider using 'write.fit'
>      or 'write' for this purpose, rather than using 'topTable' with
>      'number=nrow(fit)'.
>
> ;-P
>
> Jenny Drnevich wrote:
>> Hi Jixin,
>>
>> You're mixing up the functions topTable() and write.table() and their 
>> arguments. Try this:
>>
>>  > allgenes <- topTable(fit, number= nrow(fit), adjust="BH")
>>  > write.table( allgenes, file= "la.txt")
>>
>> HTH,
>> Jenny
>>
>> At 01:58 PM 8/12/2008, Wang, Jixin wrote:
>>> Dear all, I am using limma to analyze my microarray data. I have a 
>>> simple question to ask. I want to save the summary table of ALL 
>>> genes into local drive. I had tried to use write, write.table or 
>>> save command but I always got error message. write.table(topTable, 
>>> "la.txt") Error in as.data.frame.default(x[[i]], optional = TRUE) 
>>> :   Cannot coerce class "function" into a data.frame 
>>> write.table(fit,number=100,adjust="BH",file="la.txt") Error in 
>>> write.table(fit, number = 100, adjust = "BH", file = "la.txt") :   
>>> unused argument(s) (number = 100, adjust = "BH") When I use 
>>> write.table (fit, file="la.txt"), I got something like this: 
>>> "coefficients" "stdev.unscaled" "sigma" "df.residual" "genes.Block" 
>>> "genes.Row" "genes.Column" "genes.ID" "genes.Name" "Amean" "s2.post" 
>>> "t" "p.value" "lods" "F" "F.p.value" "1" 0.191491534183039 0.5 
>>> 0.58043881130755 3 1 1 1 5103 NA 6.32074826791933 0.303968848417341 
>>> 0.694648392759644 0.509374409393106 -4.86326047572600 
>>> 0.482536389563556 0.509374409393106 "2" -0.293787384283484 0.5 
>>> 1.20366297838960 3 1 1 2 5124 NA 8.24502874291735 0.774060196679415 
>>> -0.667845140479293 0.525351390376272 -4.87400797979679 
>>> 0.446017131661806 0.525351390376272 "3" -0.513890138933438 0.5 
>>> 0.298947585330255 3 1 1 3 5145 NA 6.24632903576038 0.199313133368442 
>>> -2.30214313044810 0.0543270719852833 -3.89174526089124 
>>> 5.29986299306939 0.0543270719852833 "4" -0.192900665446982 0.5 
>>> 0.202699077289498 3 1 1 4 5166 NA 5.4719589146908 0.178899971606802 
>>> -0.912133876540687 0.391633127283331 -4.76408991981505 
>>> 0.831988208733142 0.391633127283331 "5" -0.519787414790754 0.5 
>>> 0.971899999883875 3 1 1 5 5187 NA 4.79242047112468 0.560886278852435 
>>> -1.38809230856769 0.207129224092528 -4.49088947369315 
>>> 1.92680025710479 0.207129224092528………………….…………. What I want 
>>> is same as below (contain logFC, P Value and adj.P.Val) of ALL 
>>> genes,  options(digits=3) topTable(fit,number=100,adjust="BH")     
>>> Block Row Column    ID Name logFC AveExpr     t  P.Value 
>>> adj.P.Val     B 20830    46   2     18  5921   NA  3.22    8.42 
>>> 15.76 8.78e-07    0.0195 -1.78 5073     11  21     13 15499   NA  
>>> 3.09   11.56 12.58 4.15e-06    0.0460 -1.86 6457     14  21     11  
>>> 9451   NA  2.93   10.48 10.88 1.11e-05    0.0617 -1.93 4193     10   
>>> 2     13  9974   NA  2.69   10.25 10.87 1.11e-05    0.0617 -1.93 
>>> 292       1  14      6  5195   NA  2.22    9.67 10.35 1.55e-05    
>>> 0.0689 -1.95 12237    27  11      5 13493   NA  3.80    8.45  9.17 
>>> 3.46e-05    0.1145 -2.03 15180    33  18     22  1831   NA  4.62    
>>> 9.16 9.11 3.61e-05    0.1145 -2.03 ………………… ¦â€¦â€¦â€¦ Many thanks, 
>>> Jixin _______________________________________________ Bioconductor 
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>>
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>


-- 
Cei Abreu-Goodger, PhD

Wellcome Trust Sanger Institute
Computational and Functional Genomics
Wellcome Trust Genome Campus
Hinxton, Cambridge, CB10 1SA, UK



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