[BioC] liimma and Across Array Normalisation

[Saket Choudhary]

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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