[BioC] liimma and Across Array Normalisation

[Saket Choudhary]

On 11-Feb-2014, at 10:31 PM, Gordon K Smyth <smyth at wehi.edu.au> wrote:

> Yes, obviously there'll be a baseline shift when you subtract background, then add an offset and log transform.
>
> You plots do not appear to be a valid MA plots.
>

Could you please point out the error?
I understand a base line shoft is expected, but I cant figure out what
is going wrong otherwise.

Thanks,
Saket


> Gordon
>
> On Tue, 11 Feb 2014, 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.
>>
>> 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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