[BioC] Agilent G4112A Arrays

[Wolfgang Huber]

Hi Chuming

if you want to work with the approximation that M-values have equal 
variances, then preprocessing the data with a method that provides 
variance stabilisation (e.g. vsn) will likely be useful.

Furthermore, it might be useful to discard a fraction of genes with low 
A-values, since they are more likely to be either not expressed, or so 
weakly expressed that you would find it more difficult to validate them.

	Best wishes
	Wolfgang

Naomi Altman wrote:
> The more data one has, the fewer assumptions one needs.  In the absence 
> of replication, you cannot get p-values without very strong 
> assumptions.  e.g. you could assume that the vast majority of the genes 
> do not differentially express, that their M-values have equal variance 
> and that the M-values are normally distributed.  Then you could use e.g. 
> the IQR of the M-values to estimate the sd and use this to pick a fold 
> cut-off for DE.  You have no reasonable way to estimate FDR with this 
> approach, but it might be slightly better than using 2-fold - or then 
> again, it might not.  Without replication, there is no way to know.
> 
> Regards,
> Naomi Altman
> 
> 
> At 08:53 AM 1/25/2010, Chuming Chen wrote:
>> Hi Prashantha,
>>
>> Thank you for your suggestion. My target file is as below. Although I 
>> couldn't fit a linear model, I still wonder whether I can do some 
>> statistic on M (log ratio) values and use the p-value to get the 
>> differentially expressed genes.
>>
>> SlideNumber    FileName    Cy3    Cy5
>> 1    B1vsT1.txt    B1    T1
>> 2    B2vsT2.txt    B2    T2
>> 3    B3vsT3.txt    B3    T3
>> 4    B4vsT4.txt    B4    T4
>> 5    B5vsT5.txt    B5    T5
>>
>> Chuming
>>
>>
>> Prashantha Hebbar wrote:
>>> Dear Chen,
>>>
>>> You need not to look for any other packages. Since, you do not have 
>>> any replicates, do not fit linear model, instead just do 
>>> normalization with in arrays and look at the M (log ratio) values.
>>>
>>> Regards,
>>>
>>> Prashantha Hebbar Kiradi,
>>> Dept. of Biotechnology,
>>> Manipal Life Sciences Center,
>>> Manipal University,
>>> Manipal, India
>>>
>>>
>>> --- On *Mon, 1/25/10, Chuming Chen /<chumingchen at gmail.com>/* wrote:
>>>
>>>
>>>     From: Chuming Chen <chumingchen at gmail.com>
>>>     Subject: [BioC] Agilent G4112A Arrays
>>>     To: bioconductor at stat.math.ethz.ch
>>>     Date: Monday, January 25, 2010, 6:32 AM
>>>
>>>     Dear All,
>>>
>>>     I am trying to find out the differentially expressed genes from
>>>     some Agilent Human Whole Genome (G4112A) Arrays data.
>>>
>>>     I have tried LIMMA package, but LIMMA gave the error message "no
>>>     residual degrees of freedom in linear model fits" and stopped. My
>>>     guess is that my data has no replicates in the experiment.
>>>
>>>     Is there any other packages I can use to find differentially
>>>     expressed genes which does not require replicates in the experiment?
>>>
>>>     Thanks for your help.
>>>
>>>     Chuming
>>>
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>>
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> 
> 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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-- 

Best wishes
      Wolfgang


--
Wolfgang Huber
EMBL
http://www.embl.de/research/units/genome_biology/huber/contact