[BioC] Detection calls and LIMMA in GENE ST1.0

[Richard Friedman]

Jenny and list,

	Am I correct that P/M/A calls depend upon the presence of mismatched  
genes?
If so, is there a way to filter RMA normalized ST1.0 genes by present  
absent?

Thanks and best wishes,
Rich

	
On Mar 2, 2011, at 4:04 PM, Jenny Drnevich wrote:

> Hi Rich,
>
> You can use the P/M/A calls to filter regardless of what pre- 
> processing algorithm you use. It is a completely separate algorithm  
> from MAS5, and I routinely use the P/M/A calls from the mas5calls()  
> function in the affy package in conjunction with gcrma values.
>
> Cheers,
> Jenny
>
> At 01:14 PM 3/2/2011, Richard Friedman wrote:
>> Dear Gordon,
>>
>>        Can you  suggest how to define "some modest evidence of  
>> expression"
>> in Affymetrix arrays filtered with RMA
>> or GCRMA which does not give a presence-or-absence call?
>>
>> Thanks and best wishes,
>> Rich
>>
>> On Mar 1, 2011, at 7:50 PM, Gordon K Smyth wrote:
>>
>>> Deaer Avhena,
>>>
>>> I agree with Wolfgang that filtering is useful.  In my lab, the
>>> standard practice is to filter probes that fail to show some modest
>>> evidence for expression on at least n arrays, where n is the minimum
>>> group size.  For example, if we compare wt (with 2 replicate arrays)
>>> to a mutant (with 3 replicate arrays), we filter probes that are
>>> Present on fewer than 2 arrays.
>>>
>>> This is because we want to keep any probe that is expressed in at
>>> least one of the experimental conditions.  If a probe is expressed
>>> in one of the conditions, then it should appear consistently across
>>> the replicates for that condition.
>>>
>>> Best wishes
>>> Gordon
>>>
>>>> Date: Mon, 28 Feb 2011 11:35:42 -0500
>>>> From: avehna <avhena at gmail.com>
>>>> To: whuber at embl.de
>>>> Cc: bioconductor at r-project.org
>>>> Subject: Re: [BioC] Detection calls and LIMMA
>>>>
>>>> Dear Wolfgang,
>>>>
>>>> Thank you for your response, I agree with you. I will read the
>>>> paper now...
>>>>
>>>> Best Regards,
>>>> Avhena
>>>>
>>>> On Mon, Feb 28, 2011 at 4:38 AM, Wolfgang Huber <whuber at embl.de>
>>>> wrote:
>>>>
>>>>> Hi Avhena
>>>>>
>>>>> it is not required, but properly applied filtering can increase
>>>>> detection
>>>>> power in your experiment while still controlling type-I error  
>>>>> (false
>>>>> positives). The example you mention seems to be one that you want
>>>>> to keep
>>>>> though, since it is a good candidate for being up-regulated in the
>>>>> Treatment
>>>>> condition. One possibly reasonable criterion would be, e.g., to
>>>>> filter out
>>>>> all probesets that are called 'Absent' on all arrays. Some further
>>>>> discussion on the topic is also here:
>>>>>
>>>>> [1] Bourgon, Gentleman and Huber. Independent filtering increases
>>>>> detection
>>>>> power for high-throughput experiments. PNAS, 107(21):9546-9551,
>>>>>
>>>>>      Best wishes
>>>>>      Wolfgang
>>>>>
>>>>>
>>>>> Il Feb/28/11 6:51 AM, avehna ha scritto:
>>>>>
>>>>>> Hi All,
>>>>>>
>>>>>> I have a basic question. Is it required to filter the microarray
>>>>>> data
>>>>>> based
>>>>>> on the detection calls (A/M/P) before analyzing it with LIMMA?
>>>>>>
>>>>>> What if I have the following scenario (for example):
>>>>>>
>>>>>>                          Control Control Control          
>>>>>> Treatment
>>>>>> Treatment Treatment
>>>>>> 1367813_at           A            A             P
>>>>>> P                   P                 P
>>>>>>
>>>>>> Please note that this gene is just "present/detected"  once in  
>>>>>> the
>>>>>> Control,
>>>>>> but it is present in all the replicates of the treatment. In this
>>>>>> case:
>>>>>> what
>>>>>> would be the right thing to do? To eliminate it from the analysis
>>>>>> or keep
>>>>>> it
>>>>>> and consider it up or down depending on the signal of the
>>>>>> treatment?
>>>>>>
>>>>>> Thank a lot!
>>>>>> Avhena
>>>
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