Hi Jim,

Now I understand the problem!
But I have to normalize diagnostic microarray so I'm looking for several 
methods of normalization in order to retain the best. I can't use the 
quantile normalization because I don't know if the majority of genes are 
invariants.
I think the housekeeping genes normalization could be a possible 
normalization. I selected the 20 housekeeping genes which seem to be the 
least invariants.
I don't think the normalization with the invariantset function is 
appropriated in my case.

But if you have any suggestions, I would be glad!

Thank you very much for your help,

Sophie

Le 02/07/2012 18:31, James W. MacDonald a écrit :
> Hi Sophie,
>
> On 7/2/2012 10:35 AM, Sophie Lamarre wrote:
>> Hello Jim,
>>
>> I have 151 patients in my file and 16 417 genes without the 20 
>> housekeeping genes I need to normalize.
>> I want to try different normalization methods using housekeeping genes.
>> The classic method is to calculate the mean of the housekeeping genes 
>> (selected) by patient, and subtract this value to each genes of the 
>> same patient.
>>
>> I would try the invariant set method with my data file and my list of 
>> housekeeping genes.
>> When I read the help, one said I had to have 2 vectors: my data file 
>> to normalize and my file containing the intensities of housekeeping 
>> genes (which help me to normalize):
>
> Ah, I see. The problem here is that you misunderstand what 
> normalize.invariantset() is intended to do. It is not intended to do 
> what you want, which is to use a set of housekeeping genes to 
> normalize the data. Instead, this is really an internal function for 
> normalize.AffyBatch.invariantset().
>
> The idea here is to take one chip (which is what you did), and then 
> some artificially derived 'reference' chip that contains the same 
> number of genes as your chip (and is derived from the mean, median, 
> etc for each gene), and then determine which genes don't change 
> expression between the two, and then fit a line on those 'invariant' 
> genes, which will then be used to normalize your data. If your two 
> vectors are not the same length, you will get the error you see.
>
> This is quite different from what you want to do. I don't think there 
> are any functions to do such a simple normalization, and quite frankly 
> what you propose is neither classic nor recommended (if by classic you 
> mean 'a very common and accepted method' rather than 'what people did 
> way back in the past before they knew better').
>
> To do what you propose is just a simple application of colMeans() and 
> sweep().
>
> Best,
>
> Jim
>
>
>>
>>       Usage
>>
>> normalize.AffyBatch.invariantset(abatch, prd.td = c(0.003, 0.007),
>>                                   verbose = FALSE,
>>                                   baseline.type = 
>> c("mean","median","pseudo-mean","pseudo-median"),
>>                                   type = 
>> c("separate","pmonly","mmonly","together"))
>>
>> normalize.invariantset(data, ref, prd.td=c(0.003,0.007))
>>
>>
>>       Arguments
>>
>> |abatch|
>>
>> an|AffyBatch <AffyBatch%2dclass.html>|object.
>>
>> |data|
>>
>> a vector of intensities on a chip (to normalize to the reference).
>>
>> |ref|
>>
>> a vector of reference intensities.
>>
>>
>>
>> Thank you for your help,
>>
>> Kind Regards,
>> -- 
>> Sophie LAMARRE
>>
>>
>> Le 02/07/2012 16:12, James W. MacDonald a écrit :
>>> Hi Sophie,
>>>
>>> On 7/2/2012 8:03 AM, Sophie Lamarre wrote:
>>>> Hello,
>>>>
>>>> I try the invariantset normalization function (affy package) on my 
>>>> data:
>>>>
>>>>>   test_pat1 = 
>>>>> normalize.invariantset(data_ready_to_normalize_met1[,1],
>>>> +                                    bd_20hk_norm[,1],
>>>> +                                    prd.td=c(0.003,0.007))
>>>> Error on while ((ns.old - ns)>   50) { :
>>>>     missing value where TRUE / FALSE is required
>>>
>>> When you do
>>>
>>> data_ready_to_normalize_met1[,1]
>>>
>>>
>>> you are selecting data from only one array. It isn't possible to 
>>> figure out which probesets are invariant with only one array 
>>> (because the implication is that the probesets don't vary in any 
>>> array).
>>>
>>> Is there a particular reason that you are trying to normalize just 
>>> one array?
>>>
>>> Best,
>>>
>>> Jim
>>>
>>>
>>>
>>>>
>>>>
>>>> # My data to normalize
>>>>
>>>>>   data_ready_to_normalize_met1[1:5,1]
>>>> [1]  5.803779 11.566477  8.583049  8.531674  9.490483
>>>>
>>>> # My vector containing my 20 housekeeping genes
>>>>>   bd_20hk_norm[1:5,1]
>>>> [1] 14.92680 15.58281 15.15885 15.09599 15.23146
>>>>
>>>> My session info:
>>>>
>>>>
>>>>>   sessionInfo()
>>>> R version 2.14.1 (2011-12-22)
>>>> Platform: x86_64-redhat-linux-gnu (64-bit)
>>>>
>>>> locale:
>>>>    [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C               
>>>> LC_TIME=fr_FR.UTF-8
>>>>    [4] LC_COLLATE=fr_FR.UTF-8     LC_MONETARY=fr_FR.UTF-8    
>>>> LC_MESSAGES=fr_FR.UTF-8
>>>>    [7] LC_PAPER=C                 LC_NAME=C                  
>>>> LC_ADDRESS=C
>>>> [10] LC_TELEPHONE=C             LC_MEASUREMENT=fr_FR.UTF-8 
>>>> LC_IDENTIFICATION=C
>>>>
>>>> attached base packages:
>>>> [1] grid      stats     graphics  grDevices utils     datasets  
>>>> methods   base
>>>>
>>>> other attached packages:
>>>>    [1] affy_1.32.1           preprocessCore_1.16.0 
>>>> gplots_2.10.1         KernSmooth_2.23-7
>>>>    [5] caTools_1.13          bitops_1.0-4.1        
>>>> gdata_2.8.2           gtools_2.6.2
>>>>    [9] geneplotter_1.32.1    lattice_0.20-0        
>>>> annotate_1.32.3       AnnotationDbi_1.16.19
>>>> [13] Biobase_2.14.0        limma_3.10.3
>>>>
>>>> loaded via a namespace (and not attached):
>>>> [1] affyio_1.22.0       BiocInstaller_1.2.1 DBI_0.2-5           
>>>> IRanges_1.12.6
>>>> [5] RColorBrewer_1.0-5  RSQLite_0.11.1      tools_2.14.1        
>>>> xtable_1.7-0
>>>> [9] zlibbioc_1.0.1
>>>>
>>>>
>>>> I have no missing value:
>>>>
>>>>>   test = is.na(data_ready_to_normalize_met1[,1])
>>>>>   sum(test)
>>>> [1] 0
>>>>
>>>>
>>>>
>>>> Could you help me or give me a example in order I can resolve my 
>>>> problem?
>>>>
>>>> Thank your very much,
>>>>
>>>> Kind Regards,
>>>>
>>>> Sophie LAMARRE
>>>>
>>>>     [[alternative HTML version deleted]]
>>>>
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>>>
>>
>


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