[BioC] liimma and Across Array Normalisation

[Saket Choudhary]

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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