I have been doing a similar analysis in C. elegans, albeit only a two  
sample comparison, and have been comparing gcrma with plier. After  
analyzing each output in sam with similar cutoffs I find that, in my  
arrays, the overlap is significant but limited by plier. Greater than  
80% of the significant genes within the plier dataset are contained  
in the gcrma dataset. However, use of the plier algorithm results in  
significantly fewer significant genes. When I've looked at the raw  
(log2) data for genes that are absent in plier, but present in gcrma  
list, I saw a number of samples where one or more of the array values  
was presented as a negative number. The same probe set in the gcrma  
output has values for all replicates that look very similar. I'm not  
sure what to make of this. I'm still trying to decide which algorithm  
is ideal for our analysis?

I too would appreciate a reference, if available, that directly  
compares the various common algorithms (mas5, rma, gcrma, plier).

Thanks,
Jeff



____________________________________
Jeff Habig
Department of Biochemistry
University of Utah
20 North 1900 East
Salt Lake City, UT 84132-3201
801.587.9823



>>> Dear Bioconductor community,
>>>
>>> I've been looking for differentially expressed genes in C.  
>>> elegans after a
>>> drug treatment.
>>> There are 3 replicates of each condition and 2 conditions in  
>>> total (WT and
>>> Drug)
>>> I used limma combined with either rma or mas5. I find a very very  
>>> poor
>>> overlap in the results:
>>>
>>> - example (i) only 24 of the 100 most differentially expressed genes
>>> obtained using rma are found in
>>> the 1000 most differentially expressed genes obtained using mas5
>>> - example (ii) only 183 genes are common to the lists of the 1000  
>>> most
>>> differentially expressed genes
>>> found using both methods.
>>> (see piece of code at the end)
>>>
>>> Either
>>> 1/ I am missing something which I would'nt be surprised of, as my
>>>
>> expertise
>>
>>> is very limited.
>>>
>>> In that case I am sorry for pointing out something irrelevant and  
>>> thank
>>>
>> you
>>
>>> in advance for telling
>>> me what I'm missing,
>>>
>>> 2/ The differences in the normalization methods are really at the
>>>
>> origin of
>>
>>> the observed differences.
>>> In that case, how can I know which method is the best for my case  
>>> study?
>>> Does a helpful paper exists
>>> which explains in simple words the strengths/weaknesses of each  
>>> method?
>>>
>>> Thank you very much in advance for your help,
>>>
>>> Emmanuel
>>>
>>> -------------------------------------- CODE
>>> --------------------------------------
>>> library(affy)
>>> library(limma)
>>>
>>> # Load data into Affybatch
>>> data = ReadAffy(widget=T)
>>>
>>> # Background correction / normalization
>>> eset.rma = rma(data)
>>> eset.mas = mas5(data)
>>>
>>> # Get Expression values
>>> exp.rma = exprs(eset.rma)
>>> exp.mas = exprs(eset.mas)
>>>
>>> # --- Look for differentially expressed genes using Limma package
>>> strain = c("WT","WT","WT","Drug","Drug","Drug")
>>> design = model.matrix(~factor(strain))
>>> colnames(design) = c("WT","Drug")
>>>
>>> fit.rma = lmFit(eset.rma,design)
>>> fit.mas = lmFit(eset.mas,design)
>>>
>>> fit.rma.2 = eBayes(fit.rma)
>>> fit.mas.2 = eBayes(fit.mas)
>>>
>>> top.rma = as.numeric(rownames(topTable(fit.rma.2,n=1000)))
>>> top.mas = as.numeric(rownames(topTable(fit.mas.2,n=100)))
>>> length(intersect(top.rma,top.mas))
>>>
>>>> [1] 24
>>>>
>>>
>>> top.rma = as.numeric(rownames(topTable(fit.rma.2,n=100)))
>>> top.mas = as.numeric(rownames(topTable(fit.mas.2,n=1000)))
>>> length(intersect(top.rma,top.mas))
>>>
>>>> [1] 0
>>>>
>>>
>>>       [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> Bioconductor mailing list
>>> Bioconductor@stat.math.ethz.ch
>>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>>>
>>>
>>
>> _______________________________________________
>> Bioconductor mailing list
>> Bioconductor@stat.math.ethz.ch
>> https://stat.ethz.ch/mailman/listinfo/bioconductor
>>
>
> Naomi S. Altman                                814-865-3791 (voice)
> Associate Professor
> Dept. of Statistics                              814-863-7114 (fax)
> Penn State University                         814-865-1348  
> (Statistics)
> University Park, PA 16802-2111
>
>
>


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