Hi James,
Thanks for your quick reply.

So my last step should be
exprs<-eset-median+median1.
 Is it?

In the paper they say
"RNA fluorescent labeling reaction and hybridization were performed using
the Affymetrix Gene Chips HG-U133A and HG-U133B according to the
manufacturer’s instructions (http://www.affymetrix.com/). The arrays consist
of 22,283 (HG-U133A) and 22,645 (HG-U133B) probe sets, which together amount
to 23,583 unique genes based on Unigene build 173. Microarray analysis was
performed using Affymetrix Microarray Suite 5.0 and in-house Visual Basic
software MATRIX 1.26. Array data were median normalized, and replicate genes
were combined by averaging. Samples (or averages of samples) were then
compared against each other by calculating log ratios for each gene, and
statistical significance was presented as a p value calculated by Student’s
t test. The microarray data have been uploaded to GEO (Gene Expression
Omnibus), and the accession number is GSE-4824."

In GSE4824 in geo:
"Data processing:Data were analyzed with Microarray Suite version 5.0 (MAS
5.0) using Affymetrix default analysis settings and global scaling as
normalization method. The trimmed mean target intensity of each array was
arbitrarily set to 250".

 Could you please suggest whether my steps are correct :

Since there are 3 platforms namely GPL570 which contains 6 arrays which are
referenece profiles and other 2 i.e HG-U133A and HG-U133B contains 79
samples.

Steps as suggested by paper:
1)combine both platforms with mas5 intensity of both the platform for the
79samples from series matrix file(unlogged)
* what about the common probes between HG U133A and HG U133B(168)?
2)Add annotation with Unigene and then combine the reference profile which
is HGU133 plus2
*Do I ignore those probes which are unique to HGU133plus2 (9921) and probes
of HGU133A and HGU133B(6)
3)Then perform median normalization or median centering.
4)Then averaging the replicate genes.
5)Log ratios for each genes(fold change)
6)then perform statistical student t-test.
* During which step do I convert the expression to log2 ?
wiating for your suggestions.
Thanks,
Viritha

On Mon, Aug 29, 2011 at 1:27 PM, James W. MacDonald
<jmacdon@med.umich.edu>wrote:

> Hi Viritha,
>
>
> On 8/29/2011 12:10 PM, viritha kaza wrote:
>
>> Hi group,
>> I am trying to replicate a dataset GSE4824 from a paper.
>> There are actually 3 platforms in them. But right now I am concentrating
>> only on one platform GPL570.This contains 6 arrays.
>> I have written the code to perform Microarray Suite version 5.0 (MAS 5.0)
>> using Affymetrix default analysis settings and global scaling as
>> normalization method. The trimmed mean target intensity of each array was
>> arbitrarily set to 250.After which median normalization.
>>
>>
>> source("http://bioconductor.**org/biocLite.R<http://bioconductor.org/biocLite.R>
>>> ")
>>>
>>
>> biocLite("affy")
>>>
>>
>> library(affy)
>>>
>>
>> mydata<- ReadAffy()
>>>
>>
>> eset.mas5 = mas5(mydata,sc=250,normalize=**TRUE)
>>>
>>
>> write.exprs(eset.mas5,"**GSE4824_GPL570.txt",sep='\t')
>>>
>>
>> eset=exprs(eset.mas5)
>>>
>>
>> median = apply(eset, 2, median)
>>>
>>
>> median1=median(median)
>>> exprs<-eset/median*median1
>>>
>>
> The output from mas5() isn't log transformed, so you should be subtracting
> and adding, not dividing and multiplying.
>
> This assumes that by 'median normalization' the original authors simply
> meant median centering.
>
> Best,
>
> Jim
>
>
>  write.table(exprs,"GSE4824_**GPL570_Median.txt",sep='\t')
>>>
>>
>> Please let me know if the my code performs corectly the above task,
>> especially if last few steps would perform median normalization correctly
>> or
>> not? Also let me know if this is the right way to do median normalization.
>> Thanks,
>> Viritha
>>
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>
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> Biostatistician
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