[BioC] liimma and Across Array Normalisation

[James W. MacDonald]

Hi Saket,

On 2/11/2014 4:52 AM, Saket Choudhary wrote:
> Hello Gordon,
>
> Is there a reason to believe the MA plots should inherently be
> baseline shifted after normalisation?
>
> Raw MA: https://db.tt/kDBod1EJ
> background correction with 'nec': https://db.tt/0vVWeD21
> background correction with nec followed by normalisation: https://db.tt/f0M0rWeg
> background correction with 'normexp: https://db.tt/OJO0zea5
> background correction with normexp followed by normalisation:
> https://db.tt/rbLJmFBE
>
>
> The files are a bit heavy so might take some time to load into any pdf reader.

That's why you don't use a vector graphics format for plots with lots of 
points. Instead, use png or jpeg.

Best,

Jim


>
> Code: https://gist.github.com/saketkc/8931951
>
> Saket
>
> On 9 February 2014 20:45, Saket Choudhary <saketkc at gmail.com> wrote:
>> Related question: Similar to your case, my final topTable()'s output
>> indicates  some genes having a negative logFC, though literature
>> expects them to have a positive logFC.
>>
>> I looked up the calculations and the transition from positive to
>> negative logFC for these genes seems to happen after the
>> normalizeBetweenArrays step (irrespective of the kind of normalisation
>> I choose).
>>
>> This is a naive question again, but I am trying to understand what should be
>> a good metric to decide which method tends to give the least false
>> positives like this, given tham I have limited knowledge of which
>> genes should be up or down regulated(unlike in your case, where you
>> knew the  kind  of regulation[up/down] expected).
>>
>> Thanks,
>> Saket
>>
>>
>>
>>
>> On 9 February 2014 04:00, Gordon K Smyth <smyth at wehi.edu.au> wrote:
>>> On Sat, 8 Feb 2014, Saket Choudhary wrote:
>>>
>>>> Hello Gordon,
>>>>
>>>> I had a chance to go through the paper. I have a set of negative and
>>>> positive controls, arising out of single channel Genepix platform.
>>>>  From what I could gather, 'nec' method in limma performs
>>>> backgroundcorrection using these negative control spots.
>>>
>>> Yes, but the negative controls are assumed to behave exactly like probes for
>>> unexpressed genes.  This is true for Illumina Beadchips, but is often not
>>> the case for other platforms.  If not, then you would be better to stick
>>> with normexp as you are already using.
>>>
>>>
>>>> However one of the inputs to 'nec' is also "detection.p", which the
>>>> .gprs don't have.
>>>
>>> detection.p is not a required argument.  It is used only when negative
>>> controls are not available.
>>>
>>>
>>>> I could simply take a mean of all the negative controls E and Eb, and
>>>> subtract it from each probe's E&Eb, doing it for all the arrays. Would
>>>> this mimic what I want to acheive with the 'nec' function?
>>>
>>> No, that naive approach is not equivalent and typically performs poorly.
>>>
>>> Gordon
>>>
>>>
>>>> Saket
>>>>
>>>> On 6 February 2014 13:04, Saket Choudhary <saketkc at gmail.com> wrote:
>>>>> Hello Gordon,
>>>>>
>>>>> Unfortunately I do not have access to this as of now. I will however
>>>>> get hold of it soon.
>>>>>
>>>>> After implementing this, I would expect the 'CONTROL' to have similar,
>>>>> if not same values, right?
>>>>>
>>>>> However some of the values for these Control genes after the
>>>>> normalisebetweenarray step have high variance. Is this behaviour
>>>>> normal or am I missing something?
>>>>>
>>>>> Saket
>>>>>
>>>>> On 6 February 2014 06:32, Gordon K Smyth <smyth at wehi.edu.au> wrote:
>>>>>> If 'x' is your background-corrected EList, then
>>>>>>
>>>>>>    w <- rep(1,nrow(x))
>>>>>>    w[controls] <- 100
>>>>>>    y <- normalizeBetweenArrays(x, method="cyclicloess", weights=w)
>>>>>>
>>>>>> does what you want.
>>>>>>
>>>>>> For an example of this approach:
>>>>>>
>>>>>>    http://rnajournal.cshlp.org/content/19/7/876
>>>>>>
>>>>>> Best wishes
>>>>>> Gordon
>>>>>>
>>>>>> --------- original message ----------
>>>>>> Saket Choudhary saketkc at gmail.com
>>>>>> Thu Feb 6 06:59:42 CET 2014
>>>>>>
>>>>>> I am analysing a proteomics microarray data set for a two group
>>>>>> sample(Normal and Disease) using single color channel. The arrays have a
>>>>>> set
>>>>>> of pre-defined CONTROL points whose expression levels are supposed to be
>>>>>> similar/same across all the arrays.
>>>>>>
>>>>>> I would like to 'normalise' the levels of all probes such that
>>>>>> normalisation
>>>>>> ends up with all CONTROL points having similar expression levels. If I
>>>>>> understand it right, normalizebetweenarray does not allow this kind of
>>>>>> normalisation.
>>>>>>
>>>>>> Is there a pre-implemented function to do this? If not, what would be a
>>>>>> way
>>>>>> to acheive this kind of normalisation?
>>>>>>
>>>>>> Code: https://gist.github.com/saketkc/8669586
>>>>>>
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>>>>
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-- 
James W. MacDonald, M.S.
Biostatistician
University of Washington
Environmental and Occupational Health Sciences
4225 Roosevelt Way NE, # 100
Seattle WA 98105-6099