[BioC] removal of genes with low expression values

[Mark Cowley]

hi Hernando,

I suggest you have a read of: 
J. N. McClintick & H. J. Edenberg. Effects of filtering by Present call on analysis of microarray experiments.. BMC Bioinformatics, 2006 (7): 49.

there's no standard cutoff, but if you plot histograms of normalised data, then can help you choose a cutoff

cheers,
mark
On 07/07/2010, at 11:22 PM, Naomi Altman wrote:

> Isn't filtering on spread like pretesting for differential expression?  Maybe not such a good idea.
> 
> When using MAS5, it is traditional to use the Affy presence/absence score.  (Not necessarily optimal ...)
> 
> --Naomi
> 
> At 06:53 AM 7/7/2010, Yuan Hao wrote:
>> Usually it can be filtered based on IQR and/or variance across
>> samples, which might be worthy of thinking besides 'average'
>> 
>> Yuan
>> 
>> On 7 Jul 2010, at 11:47, Sean Davis wrote:
>> 
>>> On Wed, Jul 7, 2010 at 6:28 AM, Hernando Martínez <hernybiotec at gmail.com >wrote:
>>> 
>>>> Hi, I need to remove genes with low expression values from a
>>>> expression
>>>> matrix. I would like to remove those with an average of expression
>>>> values
>>>> less than a certain cut-off. I was thinking in computing the
>>>> average for
>>>> each row, create a list with the gene names for which their average
>>>> is less
>>>> than the cut-off, and remove those genes from the initial matrix.
>>>> However,
>>>> I
>>>> have a couple of doubts that maybe you can help me with. Is there any
>>>> package or function that makes this easier? And, does anyone know
>>>> which
>>>> cut-off to use for data normalized with RMA and for data normalized
>>>> with
>>>> MAS5? Thanks,
>>>> 
>>> Take a look at the genefilter package.  However, what you describe
>>> can be
>>> done easily with standard R.
>>> 
>>> I don't think there is such a thing as a "standard" cutoff for
>>> microarray
>>> data.
>>> 
>>> Sean
>>> 
>>>        [[alternative HTML version deleted]]
>>> 
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> 
> Naomi S. Altman                                814-865-3791 (voice)
> Associate Professor
> Dept. of Statistics                              814-863-7114 (fax)
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