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

Mon Mar 7 20:02:01 CET 2011

Dear Rich,

Please correct me if I am wrong, but the question is basically whether
any sort of estimate P/A exists with PM-only arrays? I recall a
discussion where alternative options were proposed, and as far as I
know none of the alternatives is stream-lined for all array types.

But if you want to invest a bit of effort, such a method could be
adapted for your array type using ideas from different approaches:

a) the PANP-method (available for the HGU133-series, implemented in
the PANP package available in Bioc) developed by Peter Warren and
colleagues (I have adapted a version for the ATH1-Arabidopsis chip
myself, using probes that do not match newer genome releases anymore;
this could easily be done by BLAST for example):
www.people.brandeis.edu/~dtaylor/Taylor_Papers/panp.pdf

b) the half-price method proposed by Wu and Irizarry (as far as I know
the code is available upon request):
http://www.bepress.com/jhubiostat/paper73

c) the developers of the Rosetta software have proposed a further
method to calculate present/absent p-values based on a Gaussian
distribution with parameters estimated from negative probes (Weng et
al, Bioinformatics (2006) 22 (9): 1111-1121.
http://bioinformatics.oxfordjournals.org/content/22/9/1111.full). I
guess the method has been implemented in their software? But it might
be adapted in R too...

Maybe there are further suggestions?
Hope this helps anyway...

Best wishes, Sam

On 7 March 2011 18:30, Richard Friedman
<friedman at cancercenter.columbia.edu> wrote:
> 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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>>>> 4}}
>>>>
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-------------------------------------------------------
Samuel Wuest
Smurfit Institute of Genetics
Trinity College Dublin
Dublin 2, Ireland
Phone: +353-1-896 2444
Web: http://www.tcd.ie/Genetics/wellmer-2/index.html
Email: wuests at tcd.ie