[BioC] quantile normalization of one dataset to another

[Daniel Brewer]

Just found another complicating factor.  Even though both sets are from
Affymetrix arrays they are from different types and so have different
numbers of probes.  Therefore quantile normalisation may not be the way
forward.  Are there any other cross array normalisation techniques which
would be suitable, whilst still retaining that one dataset is left
untouched?

Dan

Benilton Carvalho wrote:
> Assuming that the second dataset is quantile normalized and that its
> expression matrix is called "expression2", what you want to do is:
> 
> ref <- sort(expression2[,1])  ## if it is quantile normalized, every
> column has the same distribution
> for (i in 1:ncol(expression1))
>   expression1[order(expression1[,i]),i] <- ref
> 
> b
> 
> On May 22, 2007, at 10:17 AM, Daniel Brewer wrote:
> 
>> Hi,
>>
>> I have two different datasets which are both originally from the
>> Affymetrix platform but now I only have the expression matrix.  Both
>> datasets have an overall different distribution of expression.  What I
>> would like to do is quantile normalisation on each of the experiments in
>> one dataset to the quantile curve of the other dataset i.e. I want to
>> leave the second dataset untouched, but normalise the first to it.  ANy
>> ideas how to do this?  I looked at normalize.quantiles but it does not
>> appear to do what I want.
>>
>> Thanks
>>
>> Dan
> 
> 
> 
> -- 
> Benilton Carvalho
> PhD Candidate
> Department of Biostatistics
> Bloomberg School of Public Health
> Johns Hopkins University
> bcarvalh at jhsph.edu
> 
> 

-- 
**************************************************************

Daniel Brewer, Ph.D.

Institute of Cancer Research
Molecular Carcinogenesis
MUCRC
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Tel: +44 (0) 20 8722 4109
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Email: daniel.brewer at icr.ac.uk

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